---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- name: log: /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/cuties.txt log type: text opened on: 19 Aug 2022, 18:36:25 . . . . . . * ------------------------------------------------------------------------------ . * Paul Vaucher . * Unit of Research in Mobility & Musculoskeletal Care . * School of Health Sciences - Fribourg . * University of Applied Sciences Western Switzerland . * . * paul.vaucher@hes-so.ch . * . *---------------------------------------------------------------------------------------------- . * CUTIES trial Statistical Output version 1.4 . *---------------------------------------------------------------------------------------------- . * Version 1.0, 24.06.2022 in accordance to Statistical Plan 1.0 (Blinded) . * Version 1.1, 22.07.2022 in accordance to Statistical Plan 1.1 (Blinded) . * Version 1.2, 22.07.2022 in accordance to Statistical Plan 1.2 (Blinded) . * Version 1.3, 09.08.2022 in accordance to Statistical Plan 1.3 (Blinded) . * Version 1.4, 12.08.2022 in accordance to Statistical Plan 1.3 (Unblinded) . * Version 1.5, 17.08.2022 in accordance to Statistical Plan 1.3 (Correct group) . . . . * ------- PREPARING DATASET ---------- . . * Files to be placed in Data folder for "do" file to function: . * 1. castor.csv // Participant questionnaires from CASTOR ECD . * 2. diary.csv // Full crying diary entries hour by hour . * 3. diary_merge.csv // Summary crying diary with data only made available day by day. . * 3. sae.csv // List of reported serious adverse events . * 4. treatment1.csv // List of osteopathic manipulative treatment reported by osteopaths . * 5. treatment2.csv // List of additional care and treatments . * 6. ost.csv // Descriptive list for osteopaths . . * STATA data files that are created by this log file in the Source_files folder . * 1. diary_full.dta Hour by hour reported crying time from diaries . * 2. diary.dta Wide format for day to day diary data . * 3. diary2.dta Additional imported data for for wide format diary data . * 4. castar.dta Wide format with data from castor questionnaires . * 5. treatment.dta Merged data for advice provided to each participant . * 6. omt.dta Merged describer of type of osteopathic manipulative treatment . * 6. sea.dta Serious adverse events // Not created as only two adverse events occured . * 7. dataset_wide.dta Wide format for all data . * 8. dataset.dta Long format for all data . . . * ------ SECTION DESCRIPTION -------------- . . * 1. IMPORT DATA . * 1.1 Import diary . * 1.2 Import data from CASTOR . * 1.3 Import data for treatment . * 1.4 Import data for SAEs . * 1.5 Merge data from files for analysis . . * 2. TESTING ASSUMPTIONS . * 2.1 Testing colinearity with covariates . * 2.2 Testing the colinearity between the predicted crying time and the observed crying time . * 2.3 Ploting residuals over the crying time . * 2.4 Ploting residuals over time . * 2.5 Ploting residuals for heteroskedasticity . * 2.6 Verifying normality of distribution of residuals . * 2.7 Analysing trends . . * 3. DESCRIPTIVE STATISTICS . * 3.1 Missing data . * 3.2 Participant description . * 3.3 Baseline values for outcomes . . * 4. HYPOTHESIS TESTING . * 4.1 Primary outcome – average reduction in crying time . * 4.2 Secondary outcomes . . * 5. PATIENT MANAGEMENT . * 5.1 Number of sessions . * 5.2 Advice . * 5.3 Medication and Remedies . * 5.4 Additional care . * 5.5 Blinding . * 5.6 Provided osteopathic manual treatment . . * 6. SENSITIVITY ANALYSIS . * 6.1 Per protocol analysis . * 6.2 Log transformed analysis . * 6.3 Analysis without crying time imputation . * 6.4 Adjusting for eventual attrition bias . * 6.5 Between institution differences . * 6.6 Worse case scenario . * 6.7 Best case scenario . * 6.8 GLS random-effect model with adjustment . . * 7. FIGURES . * 7.1 Figure 2 – Reported average crying time per time slot for each group . * 7.2 Figure 3 – Average daily crying time during follow-up between groups . . . . . . . . . * ---------------------- 1. IMPORT DATA ------------------ . . . * 1.1 Import diary . import delimited "Data/diary.csv" (27 vars, 750 obs) . save Source_files/diary_full.dta, replace file Source_files/diary_full.dta saved . . . * 1.1.1 Generate unique numerical ID number for entries . sort id . rename id id_txt . generate id_centre_txt = substr(id_txt,1,3) . . /* List of codes for Centres > > SMD 4400 (Pilot data) > PO1 4401 > UCO 4402 > ESO 4403 > PNC 4404 > CH1 4101 > CH2 4102 > AU1 6101 > AU2 6102 > AU3 6103 > AU4 6104 > > */ . . gen id_centre = 0 . replace id_centre=4401 if(id_centre_txt=="P01" | id_centre_txt=="p01"| id_centre_txt=="PO1" | id_centre_txt=="p01" ) (30 real changes made) . replace id_centre=4402 if(id_centre_txt=="UCO" | id_centre_txt=="uco" ) (105 real changes made) . replace id_centre=4403 if(id_centre_txt=="ESO" | id_centre_txt=="eso" ) (135 real changes made) . replace id_centre=4404 if(id_centre_txt=="PNC" | id_centre_txt=="pnc" ) (90 real changes made) . replace id_centre=4101 if(id_centre_txt=="CH1" | id_centre_txt=="ch1" ) (45 real changes made) . replace id_centre=4102 if(id_centre_txt=="CH2" | id_centre_txt=="ch2" ) (0 real changes made) . replace id_centre=6101 if(id_centre_txt=="AU1" | id_centre_txt=="au1" ) (0 real changes made) . replace id_centre=6102 if(id_centre_txt=="AU2" | id_centre_txt=="au2" ) (0 real changes made) . replace id_centre=6103 if(id_centre_txt=="AU3" | id_centre_txt=="au3" ) (30 real changes made) . replace id_centre=6104 if(id_centre_txt=="AU4" | id_centre_txt=="au4" ) (45 real changes made) . replace id_centre=4400 if(id_centre_txt=="SMD" | id_centre_txt=="SMD" ) (270 real changes made) . . generate id_part_txt = substr(id_txt,-4,.) . replace id_part_txt=id_txt if(id_part_txt=="") (0 real changes made) . destring id_part_txt, generate(id_part) id_part_txt: all characters numeric; id_part generated as byte . gen id=id_centre*1000+id_part . sort id . drop id_centre_txt id_part_txt id_part id_txt id_centre . order id d h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 h16 h17 h18 h19 h20 h21 h22 h23 h24 h1 h2 h3 h4 h5 . save Source_files/diary_full.dta, replace file Source_files/diary_full.dta saved . . * 1.1.2 Generate dataset with daily crying time . . drop if(missing>0) (95 observations deleted) . rename d day . . gen d = h1+h2+h3+h4+h5+h6+h7+h8+h9+h10+h11+h12+h13+h14+h15+h16+h17+h18+h19+h20+h21+h22+h23+h24 . drop h1 h2 h3 h4 h5 h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 h16 h17 h18 h19 h20 h21 h22 h23 h24 missing . reshape wide d, i(id) j(day) (note: j = 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14) Data long -> wide ----------------------------------------------------------------------------- Number of obs. 655 -> 47 Number of variables 3 -> 16 j variable (15 values) day -> (dropped) xij variables: d -> d0 d1 ... d14 ----------------------------------------------------------------------------- . save Source_files/diary.dta, replace file Source_files/diary.dta saved . . . * 1.1.3 Generate imputation values for diaries without duration . use Source_files/diary_full.dta, clear . forvalues i = 1/24{ 2. replace h`i'=. if(h`i'==0) 3. } (562 real changes made, 562 to missing) (537 real changes made, 537 to missing) (541 real changes made, 541 to missing) (553 real changes made, 553 to missing) (521 real changes made, 521 to missing) (504 real changes made, 504 to missing) (458 real changes made, 458 to missing) (474 real changes made, 474 to missing) (422 real changes made, 422 to missing) (429 real changes made, 429 to missing) (448 real changes made, 448 to missing) (438 real changes made, 438 to missing) (461 real changes made, 461 to missing) (458 real changes made, 458 to missing) (429 real changes made, 429 to missing) (400 real changes made, 400 to missing) (389 real changes made, 389 to missing) (349 real changes made, 349 to missing) (348 real changes made, 348 to missing) (345 real changes made, 345 to missing) (415 real changes made, 415 to missing) (481 real changes made, 481 to missing) (554 real changes made, 554 to missing) (580 real changes made, 580 to missing) . collapse (p50) h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 h16 h17 h18 h19 h20 h21 h22 h23 h24 h1 h2 h3 h4 h5, by(d) . . forvalues i = 1/24{ 2. replace h`i'=round(h`i',1) 3. } (4 real changes made) (4 real changes made) (1 real change made) (0 real changes made) (1 real change made) (4 real changes made) (1 real change made) (3 real changes made) (1 real change made) (0 real changes made) (3 real changes made) (2 real changes made) (2 real changes made) (1 real change made) (2 real changes made) (2 real changes made) (0 real changes made) (0 real changes made) (3 real changes made) (2 real changes made) (5 real changes made) (4 real changes made) (3 real changes made) (2 real changes made) . . outsheet d h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 h16 h17 h18 h19 h20 h21 h22 h23 h24 h1 h2 h3 h4 h5 using Data/imputation.csv, comma replace . . . * 1.1.4 Add diaries without full hourly data . import delimited "Data/diary_merged.csv", clear (16 vars, 8 obs) . sort id . rename id id_txt . generate id_centre_txt = substr(id_txt,1,3) . gen id_centre = 0 . replace id_centre=4401 if(id_centre_txt=="P01" | id_centre_txt=="p01"| id_centre_txt=="PO1" | id_centre_txt=="p01" ) (0 real changes made) . replace id_centre=4402 if(id_centre_txt=="UCO" | id_centre_txt=="uco" ) (0 real changes made) . replace id_centre=4403 if(id_centre_txt=="ESO" | id_centre_txt=="eso" ) (1 real change made) . replace id_centre=4404 if(id_centre_txt=="PNC" | id_centre_txt=="pnc" ) (0 real changes made) . replace id_centre=4101 if(id_centre_txt=="CH1" | id_centre_txt=="ch1" ) (1 real change made) . replace id_centre=4102 if(id_centre_txt=="CH2" | id_centre_txt=="ch2" ) (0 real changes made) . replace id_centre=6101 if(id_centre_txt=="AU1" | id_centre_txt=="au1" ) (0 real changes made) . replace id_centre=6102 if(id_centre_txt=="AU2" | id_centre_txt=="au2" ) (0 real changes made) . replace id_centre=6103 if(id_centre_txt=="AU3" | id_centre_txt=="au3" ) (0 real changes made) . replace id_centre=6104 if(id_centre_txt=="AU4" | id_centre_txt=="au4" ) (1 real change made) . replace id_centre=4400 if(id_centre_txt=="SMD" | id_centre_txt=="SMD" ) (5 real changes made) . . generate id_part_txt = substr(id_txt,-4,.) . replace id_part_txt=id_txt if(id_part_txt=="") (0 real changes made) . destring id_part_txt, generate(id_part) id_part_txt: all characters numeric; id_part generated as byte . gen id=id_centre*1000+id_part . sort id . drop id_centre_txt id_part_txt id_part id_txt id_centre . merge 1:1 id using Source_files/diary.dta (note: variable d0 was int, now float to accommodate using data's values) (note: variable d1 was int, now float to accommodate using data's values) (note: variable d2 was int, now float to accommodate using data's values) (note: variable d3 was int, now float to accommodate using data's values) (note: variable d4 was int, now float to accommodate using data's values) (note: variable d5 was int, now float to accommodate using data's values) (note: variable d6 was int, now float to accommodate using data's values) (note: variable d7 was int, now float to accommodate using data's values) (note: variable d8 was int, now float to accommodate using data's values) (note: variable d9 was int, now float to accommodate using data's values) (note: variable d10 was int, now float to accommodate using data's values) (note: variable d11 was int, now float to accommodate using data's values) (note: variable d12 was int, now float to accommodate using data's values) (note: variable d13 was int, now float to accommodate using data's values) (note: variable d14 was int, now float to accommodate using data's values) Result # of obs. ----------------------------------------- not matched 55 from master 8 (_merge==1) from using 47 (_merge==2) matched 0 (_merge==3) ----------------------------------------- . drop _merge . order id d0 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 d13 d14 . save Source_files/diary.dta, replace file Source_files/diary.dta saved . . . * 1.1.5 Add group allocation . . import delimited "Data/group.csv", clear (5 vars, 66 obs) . sort id . rename id id_txt . generate id_centre_txt = substr(id_txt,1,3) . gen id_centre = 0 . replace id_centre=4401 if(id_centre_txt=="P01" | id_centre_txt=="p01"| id_centre_txt=="PO1" | id_centre_txt=="p01" ) (2 real changes made) . replace id_centre=4402 if(id_centre_txt=="UCO" | id_centre_txt=="uco" ) (8 real changes made) . replace id_centre=4403 if(id_centre_txt=="ESO" | id_centre_txt=="eso" ) (14 real changes made) . replace id_centre=4404 if(id_centre_txt=="PNC" | id_centre_txt=="pnc" ) (7 real changes made) . replace id_centre=4101 if(id_centre_txt=="CH1" | id_centre_txt=="ch1" ) (4 real changes made) . replace id_centre=4102 if(id_centre_txt=="CH2" | id_centre_txt=="ch2" ) (0 real changes made) . replace id_centre=6101 if(id_centre_txt=="AU1" | id_centre_txt=="au1" ) (1 real change made) . replace id_centre=6102 if(id_centre_txt=="AU2" | id_centre_txt=="au2" ) (0 real changes made) . replace id_centre=6103 if(id_centre_txt=="AU3" | id_centre_txt=="au3" ) (2 real changes made) . replace id_centre=6104 if(id_centre_txt=="AU4" | id_centre_txt=="au4" ) (3 real changes made) . replace id_centre=4400 if(id_centre_txt=="SMD" | id_centre_txt=="SMD" ) (25 real changes made) . . generate id_part_txt = substr(id_txt,-4,.) . replace id_part_txt=id_txt if(id_part_txt=="") (0 real changes made) . destring id_part_txt, generate(id_part) id_part_txt: all characters numeric; id_part generated as byte . gen id=id_centre*1000+id_part . sort id . drop id_centre_txt id_part_txt id_part id_txt id_centre . . merge 1:1 id using Source_files/diary.dta Result # of obs. ----------------------------------------- not matched 11 from master 11 (_merge==1) from using 0 (_merge==2) matched 55 (_merge==3) ----------------------------------------- . drop _merge . order id inst group d0 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 d13 d14 perprotocol deviation_txt . save Source_files/diary.dta, replace file Source_files/diary.dta saved . . . * 1.2 Import data from CASTOR . clear . import delimited "Data/castor.csv" (77 vars, 66 obs) . save Source_files/castor.dta, replace file Source_files/castor.dta saved . . * 1.2.1 Generate similar ID to diary . sort id . rename id id_txt . generate id_centre_txt = substr(id_txt,1,3) . gen id_centre = 0 . replace id_centre=4401 if(id_centre_txt=="P01" | id_centre_txt=="p01"| id_centre_txt=="PO1" | id_centre_txt=="p01" ) (2 real changes made) . replace id_centre=4402 if(id_centre_txt=="UCO" | id_centre_txt=="uco" ) (8 real changes made) . replace id_centre=4403 if(id_centre_txt=="ESO" | id_centre_txt=="eso" ) (14 real changes made) . replace id_centre=4404 if(id_centre_txt=="PNC" | id_centre_txt=="pnc" ) (7 real changes made) . replace id_centre=4101 if(id_centre_txt=="CH1" | id_centre_txt=="ch1" ) (4 real changes made) . replace id_centre=4102 if(id_centre_txt=="CH2" | id_centre_txt=="ch2" ) (0 real changes made) . replace id_centre=6101 if(id_centre_txt=="AU1" | id_centre_txt=="au1" ) (1 real change made) . replace id_centre=6102 if(id_centre_txt=="AU2" | id_centre_txt=="au2" ) (0 real changes made) . replace id_centre=6103 if(id_centre_txt=="AU3" | id_centre_txt=="au3" ) (2 real changes made) . replace id_centre=6104 if(id_centre_txt=="AU4" | id_centre_txt=="au4" ) (3 real changes made) . replace id_centre=4400 if(id_centre_txt=="SMD" | id_centre_txt=="SMD" ) (25 real changes made) . . generate id_part_txt = substr(id_txt,-4,.) . replace id_part_txt=id_txt if(id_part_txt=="") (0 real changes made) . destring id_part_txt, generate(id_part) id_part_txt: all characters numeric; id_part generated as byte . gen id=id_centre*1000+id_part . sort id . drop id_centre_txt id_part_txt id_part id_txt id_centre . order id . . . * 1.2.2 Generate Parenting Confidence scores . replace pcs_b1=2 if(pcs_b1==.) (1 real change made) . replace pcs_b9=2 if(pcs_b9==.) (1 real change made) . forvalues i = 1/15{ 2. replace pcs_b`i'=. if(pcs_b`i'==.) 3. } (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) . recode pcs_b13 0=3 1=2 2=1 3=0 (pcs_b13: 65 changes made) . gen pcs_b=pcs_b1 + pcs_b2 + pcs_b3 + pcs_b4 + pcs_b5 + pcs_b6 + pcs_b7 + /// > pcs_b8 + pcs_b9 + pcs_b10 + pcs_b11 + pcs_b12 + pcs_b13 + pcs_b14 + pcs_b15 (1 missing value generated) . . replace pcs_e1=2 if(pcs_e1==.) (8 real changes made) . replace pcs_e9=2 if(pcs_e9==.) (8 real changes made) . forvalues i = 1/15{ 2. replace pcs_e`i'=. if(pcs_e`i'==.) 3. } (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) . recode pcs_e13 0=3 1=2 2=1 3=0 (pcs_e13: 58 changes made) . gen pcs_e=pcs_e1 + pcs_e2 + pcs_e3 + pcs_e4 + pcs_e5 + pcs_e6 + pcs_e7 + /// > pcs_e8 + pcs_e9 + pcs_e10 + pcs_e11 + pcs_e12 + pcs_e13 + pcs_e14 + pcs_e15 (9 missing values generated) . . drop pcs_b1 pcs_b2 pcs_b3 pcs_b4 pcs_b5 pcs_b6 pcs_b7 /// > pcs_b8 pcs_b9 pcs_b10 pcs_b11 pcs_b12 pcs_b13 pcs_b14 pcs_b15 /// > pcs_e1 pcs_e2 pcs_e3 pcs_e4 pcs_e5 pcs_e6 pcs_e7 /// > pcs_e8 pcs_e9 pcs_e10 pcs_e11 pcs_e12 pcs_e13 pcs_e14 pcs_e15 . . gen pcs_cat=pcs_b (1 missing value generated) . recode pcs_cat (0/31 = 3) (32/35 = 2) (36/39 = 1) (40/100 =0)(.=4) (pcs_cat: 66 changes made) . label variable pcs_cat "Parenting Confidence (PCS-score)" . label define pcs_cat_ 0 "Non-clinical range" 1 "Mild clinical range" 2 "Moderate clinical range" 3 "Severe clinical range" 4 "Missing" . label value pcs_cat pcs_cat_ . . . * 1.2.4 Define variables for STATA . . label variable id "Participant ID" . rename date_b date_temp . gen date_b = daily(subinstr(date_temp, "'", "", .), "DMY", 2022) . format date_b %td . label variable date_b "Date of entry" . drop date_temp . . . gen parent=5 . replace parent=1 if(parent_rel1==1 & parent_rel2==1) (55 real changes made) . replace parent=2 if(parent_rel1==2 & parent_rel2==2) (1 real change made) . replace parent=3 if(parent_rel1==1 & parent_rel2==2) (2 real changes made) . replace parent=3 if(parent_rel1==2 & parent_rel2==1) (1 real change made) . replace parent=5 if(parent_rel1==. | parent_rel2==.) (0 real changes made) . label variable parent "Relation to child of person completing the questionnaires" . label define parent_ 1 "Mother" 2 "Father" 3 "Both" 4 "Other" 5 "Missing" . label value parent parent_ . . drop parent_rel1 parent_rel2 . . rename date_end date_temp . gen date_end = daily(subinstr(date_temp, "'", "", .), "DMY", 2022) (7 missing values generated) . format date_end %td . label variable date_end "Date of last visit" . drop date_temp . . gen days=date_end - date_b (7 missing values generated) . label variable days "Duration of follow-up (days)" . label variable age "Infant age (days)" . gen age_cat=age . recode age_cat (0/14 = 0) (15/28 =1) (29/42 =2) (43/56 =3) (57/70 =4)(71/84 =5)(85/100 =6)(.=7) (age_cat: 66 changes made) . label variable age_cat "Infant age" . label define age_cat_ 0 "1–2 weeks" 1 "3–4 weeks" 2 "5–6 weeks" 3 "7–8 weeks" 4 "9–10 weeks" 5 "11–12 weeks" 6 ">12 weeks" 7 "Missing" . label value age_cat age_cat_ . . . label variable bw "Birth weight (Kg)" . replace bw=round(bw, 0.005) // Strange multiple digits in original data -> > rounded to the closest 5g (13 real changes made) . . label variable weight_b "Weight current (Kg)" . replace weight_b=round(weight_b, 0.005) (10 real changes made) . . label variable weight_end "Weight at follow-up (Kg)" . replace weight_end=round(weight_end, 0.005) (14 real changes made) . . gen weight_change=weight_end-weight_b (13 missing values generated) . label variable weight_change "Weight change over follow-up period (Kg)" . replace weight_change=round(weight_change, 0.005) (44 real changes made) . . label variable ct_b "Reported crying time prior 24h (min)" . . replace sibling=6 if(sibling==.) (1 real change made) . label variable sibling "Number of siblings" . label define sibling_ 0 "None" 1 "One" 2 "Two" 3 "Three" 4 "Four" 5 "Five or more" 6 "Missing" . label value sibling sibling_ . . replace parent_age=9 if(parent_age==.) (1 real change made) . label variable parent_age "Parent age (years)" . label define parent_age_ 1 "18-20" 2 "21-25" 3 "26-30" 4 "31-35" 5 "36-40" /// > 6 "41-45" 7 "46-50" 8 "50 or more" 9 "Missing" . label value parent_age parent_age_ . . replace parent_type=3 if(parent_type==.) (1 real change made) . label variable parent_type "Parenting type" . label define parent_type_ 1 "Co-parenting" 2 "Parenting alone" 3 "Missing" . label value parent_type parent_type_ . . replace gender=3 if(gender==.) (1 real change made) . label variable gender "Gender" . label define gender_ 0 "Female" 1 "Male" 2 "Other" 3 "Missing" . label value gender gender_ . . replace expectation=6 if(expectation==.) (1 real change made) . label variable expectation "Expectations" . label define expectation_ 1 "Very well" 2 "Well" 3 "Unsure" 4 "Not very well" 5 "Not well" 6 "Missing" . label value expectation expectation_ . . label variable pcs_b "Parenting Confidence Score at baseline" . label variable pcs_e "Parenting Confidence Score at follow-up" . . replace gc_end=8 if(gc_end==.) (9 real changes made) . label variable gc_end "Global change at day 14" . label define gc_end_ 1 "Completely recovered" 2 "Much improved" /// > 3 "Slightly improved" 4 "No change" 5 "Slightly worse" 6 "Much worse" /// > 7 "Vastly worse" 8 "Not reported" . label value gc_end gc_end_ . . replace ur_reported=2 if(ur_reported==.) (8 real changes made) . label variable ur_reported "Concerns about symptoms" . label define ur_reported_ 0 "No" 1 "Yes" 2 "Unknown" . label value ur_reported ur_reported_ . . replace ur_type1=2 if(ur_type1==.) (1 real change made) . label variable ur_type1 "Concerns for more distress" . label value ur_type1 ur_reported_ . . replace ur_type2=2 if(ur_type2==.) (1 real change made) . label variable ur_type2 "Concerns for more crying" . label value ur_type2 ur_reported_ . . replace ur_type3=2 if(ur_type3==.) (1 real change made) . label variable ur_type3 "Concerns for more unsettled" . label value ur_type3 ur_reported_ . . replace ur_type4=2 if(ur_type4==.) (1 real change made) . label variable ur_type4 "Concerns for more vomiting" . label value ur_type4 ur_reported_ . . replace ur_type5=2 if(ur_type5==.) (1 real change made) . label variable ur_type5 "Concerns for difficulties feeding" . label value ur_type5 ur_reported_ . . replace ur_type6=2 if(ur_type6==.) (1 real change made) . label variable ur_type6 "Concerns for difficulties sleeping" . label value ur_type6 ur_reported_ . . replace ur_type7=2 if(ur_type7==.) (25 real changes made) . label variable ur_type7 "Other concerns" . label value ur_type7 ur_reported_ . . label variable ur_type7_txt "Details for other concerns" . . . replace other_care1=2 if(other_care1==.) (1 real change made) . label variable other_care1 "Other care - GP" . label value other_care1 ur_reported_ . . replace other_care2=2 if(other_care2==.) (1 real change made) . label variable other_care2 "Other care - A&E" . label value other_care2 ur_reported_ . . replace other_care3=2 if(other_care3==.) (1 real change made) . label variable other_care3 "Other care - Unplanned hospital admission" . label value other_care3 ur_reported_ . . replace other_care4=2 if(other_care4==.) (1 real change made) . label variable other_care4 "Other care - Other osteopath" . label value other_care4 ur_reported_ . . replace other_care5=2 if(other_care5==.) (1 real change made) . label variable other_care5 "Other care - Paediatrician" . label value other_care5 ur_reported_ . . replace other_care6=2 if(other_care6==.) (1 real change made) . label variable other_care6 "Other care - Pharmacistn" . label value other_care6 ur_reported_ . . replace other_care7=2 if(other_care7==.) (25 real changes made) . label variable other_care7 "Other care - Lactation consultant or midwife" . label value other_care7 ur_reported_ . . replace other_care8=2 if(other_care8==.) (2 real changes made) . label variable other_care8 "Other care - Other" . label value other_care8 ur_reported_ . . label variable other_care8_txt "Details for other care" . . gen other_care=. (66 missing values generated) . replace other_care=1 if(other_care1==1 | other_care2==1 | other_care3==1 | other_care4==1 | other_care5==1 | other_care6==1 | other_care7==1 | other_care8==1) (43 real changes made) . replace other_care=2 if(other_care1==0 & other_care2==0 & other_care3==0 & other_care4==0 & other_care5==0 & other_care6==0 & other_care7==0 & other_care8==0) (7 real changes made) . replace other_care=3 if(other_care==.) (16 real changes made) . label variable other_care "Other care" . label define other_care_ 1 "Received other care" 2 "Did not receive other care" 3 "Unknown if other care was provided" . label value other_care other_care_ . . . replace pharm1=2 if(pharm1==.) (1 real change made) . label variable pharm1 "Pharm & supplements - Prescribed Medicine" . label value pharm1 ur_reported_ . . replace pharm2=2 if(pharm2==.) (1 real change made) . label variable pharm2 "Pharm & supplements - Anti-gas drops" . label value pharm2 ur_reported_ . . replace pharm3=2 if(pharm3==.) (1 real change made) . label variable pharm3 "Pharm & supplements - Herbal supplements" . label value pharm3 ur_reported_ . . replace pharm4=2 if(pharm4==.) (1 real change made) . label variable pharm4 "Pharm & supplements - Probiotics" . label value pharm4 ur_reported_ . . replace pharm5=2 if(pharm5==.) (1 real change made) . label variable pharm5 "Pharm & supplements - Non-cow or anti-allergenic formula drink" . label value pharm5 ur_reported_ . . replace pharm6=2 if(pharm6==.) (1 real change made) . label variable pharm6 "Pharm & supplements - Omeprazol" . label value pharm6 ur_reported_ . . replace pharm7=2 if(pharm7==.) (1 real change made) . label variable pharm7 "Pharm & supplements - Other" . label value pharm7 ur_reported_ . . label variable pharm7_txt "Details for other Medicine or Remedies" . . replace help_belief_end=2 if(help_belief_end==.) (9 real changes made) . label variable help_belief_end "Parent's belief the treatment helped their child" . label define help_belief_end_ 0 "No" 1 "Yes" 2 "None" . label value help_belief_end help_belief_end_ . . replace experience_end=6 if(experience_end==.) (8 real changes made) . label variable experience_end "Parent's experience of care" . label define experience_end_ 1 "very good" 2 "fairly good" 3 "neither good nor bad" 4 "fairly poor" 5 "very poor" 6 "Did not respond" . label value experience_end experience_end_ . . gen experience_vg=experience_end . recode experience_vg 2/6=0 (experience_vg: 23 changes made) . label variable experience_vg "Parent's reported having had a very good experience of care" . label value experience_vg ur_reported_ . . replace satisfaction_end=6 if(satisfaction_end==.) (8 real changes made) . label variable satisfaction_end "Satisfaction with care" . label define satisfaction_end_ 1"very satisfied" 2"fairly satisfied" 3"Neither satisfied nor dissatisfied" 4"fairly dissatisfied" 5"very dissatisfied" 6 "Did not respond" . label value satisfaction_end satisfaction_end_ . . replace allocation_guess=3 if(allocation_guess==.) (8 real changes made) . label variable allocation_guess "Group allocation guess" . label define allocation_guess_ 0 "Control treatment (TTR)" 1 "Test treatment (GTR)" 2 "Don’t know/unsure" 3 "Did not respond" . label value allocation_guess allocation_guess_ . . save Source_files/castor.dta, replace file Source_files/castor.dta saved . . * 1.3 Import data for treatment . clear . import delimited "Data/treatment2.csv" (11 vars, 149 obs) . . sort id . rename id id_txt . generate id_centre_txt = substr(id_txt,1,3) . . gen id_centre = 0 . replace id_centre=4401 if(id_centre_txt=="P01" | id_centre_txt=="p01" | id_centre_txt=="PO1" | id_centre_txt=="po1") (9 real changes made) . replace id_centre=4402 if(id_centre_txt=="UCO" | id_centre_txt=="uco" ) (17 real changes made) . replace id_centre=4403 if(id_centre_txt=="ESO" | id_centre_txt=="eso" ) (23 real changes made) . replace id_centre=4404 if(id_centre_txt=="PNC" | id_centre_txt=="pnc" ) (15 real changes made) . replace id_centre=4101 if(id_centre_txt=="CH1" | id_centre_txt=="ch1" ) (9 real changes made) . replace id_centre=4102 if(id_centre_txt=="CH2" | id_centre_txt=="ch2" ) (0 real changes made) . replace id_centre=6101 if(id_centre_txt=="AU1" | id_centre_txt=="au1" ) (0 real changes made) . replace id_centre=6102 if(id_centre_txt=="AU2" | id_centre_txt=="au2" ) (0 real changes made) . replace id_centre=6103 if(id_centre_txt=="AU3" | id_centre_txt=="au3" ) (6 real changes made) . replace id_centre=6104 if(id_centre_txt=="AU4" | id_centre_txt=="au4" ) (12 real changes made) . replace id_centre=4400 if(id_centre_txt=="SMD" | id_centre_txt=="SMD" ) (58 real changes made) . . generate id_part_txt = substr(id_txt,-4,.) . replace id_part_txt=id_txt if(id_part_txt=="") (0 real changes made) . destring id_part_txt, generate(id_part) id_part_txt: all characters numeric; id_part generated as byte . gen id=id_centre*1000+id_part . sort id . drop id_centre_txt id_part_txt id_part id_txt id_centre . order id . drop visit . gen visits=1 . collapse (max) ad_sleep ad_feeding ad_diet ad_handling ad_env ad_beh ad_reas ad_other (count) visit, by(id) . save Source_files/treatment.dta, replace file Source_files/treatment.dta saved . . clear . import delimited "Data/treatment1.csv" (17 vars, 82 obs) . drop visit . gen visits=1 . collapse (max) ttt_jart ttt_blt ttt_btt_c ttt_btt_tpl ttt_btt_v ttt_btt_0 ttt_cff ttt_pff ttt_lfm ttt_count ttt_ind ttt_myor ttt_st ttt_visc ttt_other (count) visit, by(id) . save Source_files/omt.dta, replace file Source_files/omt.dta saved . outsheet id visits ttt_jart ttt_blt ttt_btt_c ttt_btt_tpl ttt_btt_v ttt_btt_0 ttt_cff ttt_pff ttt_lfm ttt_count ttt_ind ttt_myor ttt_st ttt_visc ttt_other using Data/omt.csv, > comma replace . . . * 1.4 Import data for SAEs . // Not provided for analysis as there were only 2 reported SAE. . . * 1.5 Merge data from files for analysis . . * 1.5.1 Add group allocation and baseline crying time from diary . . use Source_files/castor.dta, clear . . merge 1:1 id using Source_files/diary.dta Result # of obs. ----------------------------------------- not matched 0 matched 66 (_merge==3) ----------------------------------------- . label variable inst "Recruiting institution" . label define inst_ 0 "UK private practices" 1 "ESO" 2 "UCO" 3 "Swiss private practices" 4 "Australian private practices", replace . label value inst inst_ . label variable group "Group allocation" . label define group_ 0 "Control group (GTR)" 1 "Test group (TTR)" . label value group group_ . label variable perprotocol "Conform to protocol" . label define perprotocol_ 0 "No" 1 "Yes" . label value perprotocol perprotocol_ . label variable d0 "Crying time day before treatment (minutes)" . label variable d1 "Crying time on treatment day (minutes)" . label variable d2 "Crying time one day after treatment (minutes)" . label variable d3 "Crying time two days after treatment (minutes)" . label variable d4 "Crying time three days after treatment (minutes)" . label variable d5 "Crying time four days after treatment (minutes)" . label variable d6 "Crying time five days after treatment (minutes)" . label variable d7 "Crying time six days after treatment (minutes)" . label variable d8 "Crying time seven days after treatment (minutes)" . label variable d9 "Crying time eight days after treatment (minutes)" . label variable d10 "Crying time nine days after treatment (minutes)" . label variable d11 "Crying time 11 days after treatment (minutes)" . label variable d12 "Crying time 12 days after treatment (minutes)" . label variable d13 "Crying time 13 days after treatment (minutes)" . label variable d14 "Crying time 14 days after treatment (minutes)" . drop deviation_txt _merge . . . * 1.5.2 Add advice (treatment2) . merge 1:1 id using Source_files/treatment.dta Result # of obs. ----------------------------------------- not matched 4 from master 3 (_merge==1) from using 1 (_merge==2) matched 63 (_merge==3) ----------------------------------------- . . label variable visits "Number of sessions" . . label variable ad_sleep "Advice – Managing the baby’s sleep pattern" . replace ad_sleep=2 if(ad_sleep==.) (5 real changes made) . label define advice_ 0 "No" 1 "Yes" 2 "Not reported" . label value ad_sleep advice_ . . label variable ad_feeding "Advice – Managing the baby’s feeding pattern" . replace ad_feeding=2 if(ad_feeding==.) (4 real changes made) . label value ad_feeding advice_ . . label variable ad_diet "Advice – Mother’s diet" . replace ad_diet=2 if(ad_diet==.) (4 real changes made) . label value ad_diet advice_ . . label variable ad_handling "Advice – Handling the baby" . replace ad_handling=2 if(ad_handling==.) (3 real changes made) . label value ad_handling advice_ . . label variable ad_env "Advice – Managing the baby’s environment" . replace ad_env=2 if(ad_env==.) (4 real changes made) . label value ad_env advice_ . . label variable ad_beh "Advice – Parenting behaviour" . replace ad_beh=2 if(ad_beh==.) (4 real changes made) . label value ad_beh advice_ . . label variable ad_reas "Advice – Reassure parent/guardian" . replace ad_reas=2 if(ad_reas==.) (3 real changes made) . label value ad_reas advice_ . . label variable ad_other "Advice – Other" . replace ad_other=2 if(ad_other==.) (4 real changes made) . label value ad_other advice_ . . . order id pract group allocation_guess perprotocol date_b date_end days visits parent gender age /// > age_cat bw weight_b weight_end weight_change ct_b sibling parent_age /// > parent_type expectation d0 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 /// > d13 d14 pcs_b pcs_e pcs_cat help_belief_end experience_end /// > experience_vg satisfaction_end gc_end ur_reported ur_type1 ur_type2 ur_type3 /// > ur_type4 ur_type5 ur_type6 ur_type7_txt /// > ad_sleep ad_feeding ad_diet ad_handling ad_env ad_beh ad_reas ad_other /// > other_care other_care1 other_care2 /// > other_care3 other_care4 other_care5 other_care6 other_care7 other_care8 /// > other_care8_txt pharm1 pharm2 pharm3 pharm4 pharm5 pharm6 pharm7 pharm7_txt . . gen origin_b = 0 . replace origin_b=1 if(d0==.) (31 real changes made) . label variable origin_b "Source for baseline crying time" . label define origin_b_ 0 "Diary" 1 "Castor questionnaire" . label value origin_b origin_b_ . . replace d0=ct_b if(d0==.) (30 real changes made) . gen baseline = d0 (1 missing value generated) . label variable baseline "Crying time day before treatment (minutes)" . . save Source_files/dataset_wide.dta, replace file Source_files/dataset_wide.dta saved . . . * 1.6 Change to long format . . reshape long d, i(id) j(day) (note: j = 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14) Data wide -> long ----------------------------------------------------------------------------- Number of obs. 67 -> 1005 Number of variables 84 -> 71 j variable (15 values) -> day xij variables: d0 d1 ... d14 -> d ----------------------------------------------------------------------------- . rename d crying_time . . label variable crying_time "Daily crying time (minutes)" . label variable day "Days within trial" . . drop _merge . . save Source_files/dataset.dta, replace file Source_files/dataset.dta saved . . . . * ---------------------- 2. TESTING ASSUMPTIONS ------------------ . . /* Testing assumptions for repeated measures random linear mixed effect: > • The explanatory variables are related linearly to the response. > • The errors have constant variance. > • The errors are independent. > • The errors are Normally distributed. > > How to check the assumptions. > • Plotting the residuals against the explanatory variable will indicate if > the wrong model has been fitted (i.e. higher order terms are needed) or if > there is some dependence on some other explanatory variable. If this is the > case some obvious patterning will be visible in the plot. > • Plotting the residuals in order, any trend visible may indicate seasonal > pattern or autocorrelation. > • Plotting the residuals against the fitted values will indicate if there > is non-constant error variance, i.e. if the variance increases with the > mean the residuals will fan out as the fitted value increases. Usually > transforming the data, or using another distribution will help. > • A Normal probability plot, histogram of the residuals or say a > Wilk-Shapiro test will indicate if the normality assumption is valid, > however high non-normality should have been picked up from exploring the > data initially */ . . . * 2.1 Testing colinearity with covariates . // Testing colinearity with baseline crying time . graph twoway (lfitci crying_time baseline if(day>1)) (scatter crying_time baseline if(day>1)) . mixed crying_time baseline if(day>1) || id:, var reml // Strength of the association Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3805.7626 Iteration 1: log restricted-likelihood = -3805.7626 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(1) = 39.57 Log restricted-likelihood = -3805.7626 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- baseline | .3603181 .0572768 6.29 0.000 .2480577 .4725785 _cons | 35.28537 14.66679 2.41 0.016 6.538988 64.03176 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 1760.823 394.6087 1134.9 2731.958 -----------------------------+------------------------------------------------ var(Residual) | 3237.989 182.1984 2899.873 3615.527 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 176.35 Prob >= chibar2 = 0.0000 . . // Testing colinearity with age . graph twoway (lfitci crying_time age if(day>1)) (scatter crying_time age if(day>1)) . mixed crying_time i.age_cat if(day>1)|| id:, var reml // Strength of the association Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3793.3061 Iteration 1: log restricted-likelihood = -3793.3061 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(5) = 7.14 Log restricted-likelihood = -3793.3061 Prob > chi2 = 0.2106 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age_cat | 3–4 weeks | -20.77681 61.17576 -0.34 0.734 -140.6791 99.12548 5–6 weeks | -18.40257 58.91349 -0.31 0.755 -133.8709 97.06575 7–8 weeks | -42.26335 59.44357 -0.71 0.477 -158.7706 74.2439 9–10 weeks | -76.37426 62.31013 -1.23 0.220 -198.4999 45.75135 11–12 weeks | -96.15385 81.55167 -1.18 0.238 -255.9922 63.6845 | _cons | 152.3077 57.66574 2.64 0.008 39.28492 265.3305 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3076.3 675.9191 1999.882 4732.092 -----------------------------+------------------------------------------------ var(Residual) | 3237.487 182.144 2899.471 3614.91 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 291.00 Prob >= chibar2 = 0.0000 . mixed crying_time age_cat if(day>1)|| id:, var reml // Strength of the association Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3812.8959 Iteration 1: log restricted-likelihood = -3812.8959 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(1) = 6.21 Log restricted-likelihood = -3812.8959 Prob > chi2 = 0.0127 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age_cat | -19.24868 7.72706 -2.49 0.013 -34.39344 -4.103922 _cons | 165.0647 19.91034 8.29 0.000 126.0411 204.0882 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 2903.732 616.1335 1915.768 4401.191 -----------------------------+------------------------------------------------ var(Residual) | 3237.466 182.1415 2899.453 3614.883 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 291.69 Prob >= chibar2 = 0.0000 . . // Testing colinearity with expectation . mixed crying_time i.expectation if(day>1) || id:, var reml // Strength of the association Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3794.3356 Iteration 1: log restricted-likelihood = -3794.3356 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(5) = 5.12 Log restricted-likelihood = -3794.3356 Prob > chi2 = 0.4020 -------------------------------------------------------------------------------- crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---------------+---------------------------------------------------------------- expectation | Well | 88.42469 45.00194 1.96 0.049 .22251 176.6269 Unsure | 84.00938 43.56128 1.93 0.054 -1.369153 169.3879 Not very well | 82.51363 49.7263 1.66 0.097 -14.94812 179.9754 Not well | 118.6969 54.23457 2.19 0.029 12.39906 224.9947 Missing | 76.49557 59.52241 1.29 0.199 -40.16621 193.1573 | _cons | 35.56163 42.17006 0.84 0.399 -47.09016 118.2134 -------------------------------------------------------------------------------- ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3204.086 700.3967 2087.545 4917.818 -----------------------------+------------------------------------------------ var(Residual) | 3237.24 182.1176 2899.271 3614.606 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 305.48 Prob >= chibar2 = 0.0000 . gen expectation2=expectation (15 missing values generated) . recode expectation2 1=0 2=1 6=2 4=3 5=4 (expectation2: 480 changes made) . label variable expectation2 "Expectations" . label define expectation2_ 0 "Very well" 1 "Well" 2 "Unsure / missing" 3 "Not very well" 4 "Not well" . label value expectation2 expectation2_ . graph twoway (lfitci crying_time expectation2 if(day>1)) (scatter crying_time expectation2 if(day>1)) . mixed crying_time expectation2 if(day>1) || id:, var reml // Strength of the association Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3815.3381 Iteration 1: log restricted-likelihood = -3815.3381 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(1) = 1.09 Log restricted-likelihood = -3815.3381 Prob > chi2 = 0.2956 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- expectation2 | 8.024651 7.671712 1.05 0.296 -7.011629 23.06093 _cons | 99.92554 20.0626 4.98 0.000 60.60357 139.2475 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3207.443 674.2179 2124.383 4842.672 -----------------------------+------------------------------------------------ var(Residual) | 3237.081 182.0993 2899.145 3614.408 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 322.74 Prob >= chibar2 = 0.0000 . save Source_files/dataset.dta, replace file Source_files/dataset.dta saved . . // Testing colineartity with days within trial . graph twoway (lfitci crying_time day if(day>1)) (scatter crying_time day if(day>1)) . mixed crying_time i.day if(day>1)|| id:, var reml // Strength of the association Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3750.9036 Iteration 1: log restricted-likelihood = -3750.9036 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(12) = 64.89 Log restricted-likelihood = -3750.9036 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- day | 3 | -9.214223 10.572 -0.87 0.383 -29.93496 11.50651 4 | -1.104345 10.57441 -0.10 0.917 -21.8298 19.62111 5 | -17.70586 10.5281 -1.68 0.093 -38.34055 2.928829 6 | -27.10572 10.58225 -2.56 0.010 -47.84655 -6.364893 7 | -27.67919 10.52935 -2.63 0.009 -48.31633 -7.042046 8 | -21.53104 10.52935 -2.04 0.041 -42.16818 -.8938983 9 | -39.67838 10.52763 -3.77 0.000 -60.31217 -19.0446 10 | -37.44757 10.64042 -3.52 0.000 -58.30241 -16.59272 11 | -42.30104 10.52866 -4.02 0.000 -62.93684 -21.66525 12 | -46.0794 10.6987 -4.31 0.000 -67.04846 -25.11034 13 | -45.54845 10.63924 -4.28 0.000 -66.40098 -24.69592 14 | -53.79323 10.81772 -4.97 0.000 -74.99557 -32.59089 | _cons | 147.2208 10.68376 13.78 0.000 126.281 168.1606 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3230.114 668.7482 2152.727 4846.707 -----------------------------+------------------------------------------------ var(Residual) | 2987.348 169.675 2672.634 3339.122 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 348.92 Prob >= chibar2 = 0.0000 . mixed crying_time day if(day>1)|| id:, var reml // Strength of the association Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3789.7616 Iteration 1: log restricted-likelihood = -3789.7616 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(1) = 60.08 Log restricted-likelihood = -3789.7616 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- day | -4.351543 .5614272 -7.75 0.000 -5.45192 -3.251166 _cons | 153.6031 9.104899 16.87 0.000 135.7578 171.4483 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3234.455 669.1674 2156.247 4851.811 -----------------------------+------------------------------------------------ var(Residual) | 2960.467 166.6769 2651.165 3305.854 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 354.35 Prob >= chibar2 = 0.0000 . . * 2.2 Testing the colinearity between the predicted crying time and the observed crying time . mixed crying_time group baseline age_cat expectation2 day if(day>1) || id:, var reml // Models effects Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3763.1735 Iteration 1: log restricted-likelihood = -3763.1735 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(5) = 114.81 Log restricted-likelihood = -3763.1735 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 2.191086 11.54348 0.19 0.849 -20.43371 24.81589 baseline | .3691646 .0587057 6.29 0.000 .2541037 .4842256 age_cat | -17.0678 5.892208 -2.90 0.004 -28.61632 -5.519285 expectation2 | -4.343393 6.100005 -0.71 0.476 -16.29918 7.612398 day | -4.351613 .5612858 -7.75 0.000 -5.451713 -3.251513 _cons | 117.7239 21.88063 5.38 0.000 74.8387 160.6092 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 1546.397 359.4884 980.4906 2438.925 -----------------------------+------------------------------------------------ var(Residual) | 2961.684 166.8095 2652.143 3307.353 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 159.86 Prob >= chibar2 = 0.0000 . predict fitted if(day>1) (option xb assumed) (147 missing values generated) . twoway /// > scatter fitted crying_time if(day>1), by(group) scheme(lean2) mcolor(%30) /// > xla(0 "0" 480 "480" 60(60)420, format(%02.0f) angle(45)) /// > xtitle("Observed crying time (minutes)") /// > yla(0 "0" 300 "300" 60(60)240, format(%02.0f) ang(h)) /// > ytitle("Fitted crying time (minutes)") || /// > lowess fitted crying_time if(day>1), by(group, legend(off)) . graph export Figures/FigureAa-ResidualCryingTime.pdf, replace // Plots residuals depending of crying time (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureAa-ResidualCryingTime.pdf written in PDF format) . pwcorr fitted crying_time if(day>1), sig | fitted crying~e -------------+------------------ fitted | 1.0000 | | crying_time | 0.5706 1.0000 | 0.0000 | . drop fitted . . * 2.2.1 Log transformation . gen ct_ln = ln(2.718+crying_time) (203 missing values generated) . gen baseline_ln = ln(2.718+baseline) (15 missing values generated) . mixed ct_ln group baseline_ln age_cat expectation2 day if(day>1) || id:, var reml // Models effects Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -617.44311 Iteration 1: log restricted-likelihood = -617.44311 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(5) = 125.85 Log restricted-likelihood = -617.44311 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ ct_ln | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -.0282336 .1288626 -0.22 0.827 -.2807995 .2243324 baseline_ln | .609172 .1312342 4.64 0.000 .3519576 .8663863 age_cat | -.2386402 .0654897 -3.64 0.000 -.3669976 -.1102828 expectation2 | .0295362 .0674373 0.44 0.661 -.1026385 .1617109 day | -.0518312 .0055272 -9.38 0.000 -.0626644 -.040998 _cons | 2.210387 .6798479 3.25 0.001 .8779093 3.542864 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | .1995745 .0447015 .1286619 .3095711 -----------------------------+------------------------------------------------ var(Residual) | .2870769 .0161652 .2570795 .3205745 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 218.10 Prob >= chibar2 = 0.0000 . predict fitted if(day>1) (option xb assumed) (147 missing values generated) . twoway /// > scatter fitted ct_ln if(day>1), by(group) scheme(lean2) mcolor(%30) /// > xla(1 "0" 2.044 "5" 2.875 "15" 3.488 "30" 4.139 "60" 4.81 "120" 5.492 "240" 6.047 "480", angle(90)) /// > xtitle("Observed crying time (minutes)") /// > yla(2.875 "15" 3.488 "30" 4.139 "60" 4.81 "120" 5.208 "180" 5.492 "240" , ang(h)) /// > ytitle("Fitted crying time (minutes)") || /// > lowess fitted ct_ln if(day>1), by(group, legend(off)) . graph export Figures/FigureAb-ResidualCryingTime.pdf, replace // Plots residuals depending of crying time (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureAb-ResidualCryingTime.pdf written in PDF format) . pwcorr fitted ct_ln if(day>1), sig | fitted ct_ln -------------+------------------ fitted | 1.0000 | | ct_ln | 0.5434 1.0000 | 0.0000 | . drop fitted . . * 2.3 Ploting residuals over the crying time . mixed crying_time group baseline age_cat expectation2 day if(day>1) || id:, var reml // Models effects Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3763.1735 Iteration 1: log restricted-likelihood = -3763.1735 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(5) = 114.81 Log restricted-likelihood = -3763.1735 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 2.191086 11.54348 0.19 0.849 -20.43371 24.81589 baseline | .3691646 .0587057 6.29 0.000 .2541037 .4842256 age_cat | -17.0678 5.892208 -2.90 0.004 -28.61632 -5.519285 expectation2 | -4.343393 6.100005 -0.71 0.476 -16.29918 7.612398 day | -4.351613 .5612858 -7.75 0.000 -5.451713 -3.251513 _cons | 117.7239 21.88063 5.38 0.000 74.8387 160.6092 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 1546.397 359.4884 980.4906 2438.925 -----------------------------+------------------------------------------------ var(Residual) | 2961.684 166.8095 2652.143 3307.353 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 159.86 Prob >= chibar2 = 0.0000 . predict r if(day>1), residuals (318 missing values generated) . twoway /// > scatter r crying_time if(day>1), by(group) scheme(lean2) mcolor(%30) /// > xla(0 "0" 480 "480" 60(60)420, format(%02.0f) angle(45)) /// > xtitle("Observed crying time (minutes)") /// > yla(-300 "-300" 300 "300" -240(60)240, format(%02.0f) ang(h)) /// > ytitle("Residuals (minutes)") || /// > lowess r crying_time if(day>1), by(group, legend(off)) . graph export Figures/FigureBa-ResidualCryingTime.pdf, replace // Plots residuals depending of crying time (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureBa-ResidualCryingTime.pdf written in PDF format) . pwcorr r crying_time if(day>1), sig | r crying~e -------------+------------------ r | 1.0000 | | crying_time | 0.7009 1.0000 | 0.0000 | . drop r . . * 2.3.1 Log transformation . mixed ct_ln group baseline_ln age_cat expectation2 day if(day>1) || id:, var reml // Models effects Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -617.44311 Iteration 1: log restricted-likelihood = -617.44311 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(5) = 125.85 Log restricted-likelihood = -617.44311 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ ct_ln | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -.0282336 .1288626 -0.22 0.827 -.2807995 .2243324 baseline_ln | .609172 .1312342 4.64 0.000 .3519576 .8663863 age_cat | -.2386402 .0654897 -3.64 0.000 -.3669976 -.1102828 expectation2 | .0295362 .0674373 0.44 0.661 -.1026385 .1617109 day | -.0518312 .0055272 -9.38 0.000 -.0626644 -.040998 _cons | 2.210387 .6798479 3.25 0.001 .8779093 3.542864 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | .1995745 .0447015 .1286619 .3095711 -----------------------------+------------------------------------------------ var(Residual) | .2870769 .0161652 .2570795 .3205745 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 218.10 Prob >= chibar2 = 0.0000 . predict r if(day>1), residuals (318 missing values generated) . twoway /// > scatter r ct_ln if(day>1), by(group) scheme(lean2) mcolor(%30) /// > xla(1 "0" 2.044 "5" 2.875 "15" 3.488 "30" 4.139 "60" 4.81 "120" 5.492 "240" 6.047 "480", angle(90)) /// > xtitle("Observed crying time (minutes)") /// > ytitle("Residuals (ln(minutes))") || /// > lowess r ct_ln if(day>1), by(group, legend(off)) . graph export Figures/FigureBb-ResidualCryingTime.pdf, replace // Plots residuals depending of crying time (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureBb-ResidualCryingTime.pdf written in PDF format) . pwcorr r ct_ln if(day>1), sig | r ct_ln -------------+------------------ r | 1.0000 | | ct_ln | 0.6809 1.0000 | 0.0000 | . drop r . . . * 2.4 Ploting residuals over time . mixed crying_time group baseline age_cat expectation2 day if(day>1) || id:, var reml // Models effects Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3763.1735 Iteration 1: log restricted-likelihood = -3763.1735 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(5) = 114.81 Log restricted-likelihood = -3763.1735 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 2.191086 11.54348 0.19 0.849 -20.43371 24.81589 baseline | .3691646 .0587057 6.29 0.000 .2541037 .4842256 age_cat | -17.0678 5.892208 -2.90 0.004 -28.61632 -5.519285 expectation2 | -4.343393 6.100005 -0.71 0.476 -16.29918 7.612398 day | -4.351613 .5612858 -7.75 0.000 -5.451713 -3.251513 _cons | 117.7239 21.88063 5.38 0.000 74.8387 160.6092 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 1546.397 359.4884 980.4906 2438.925 -----------------------------+------------------------------------------------ var(Residual) | 2961.684 166.8095 2652.143 3307.353 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 159.86 Prob >= chibar2 = 0.0000 . predict r if(day>1), residuals (318 missing values generated) . predict fitted if(day>1) (option xb assumed) (147 missing values generated) . . twoway /// > scatter r day if(day>1), by(group) scheme(lean2) mcolor(%30) /// > yla(-300 "-300" 300 "300" -240(60)240, format(%02.0f) ang(h)) /// > ytitle("Residuals (minutes)") /// > xla(2(1)13, format(%02.0f) ang(h)) /// > xtitle("Days within trial") || /// > lowess r day if(day>1), by(group, legend(off)) . graph export Figures/FigureC-ResidualDays.pdf, replace // depending of the duration of the follow-up (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureC-ResidualDays.pdf written in PDF format) . . * 2.5 Ploting residuals for heteroskedasticity . twoway /// > scatter r fitted if(day>1), by(group) scheme(lean2) mcolor(%30) /// > yla(-300 "-300" 300 "300" -240(60)240, format(%02.0f) ang(h)) /// > ytitle("Residuals (minutes)") /// > xla(0 "0" 300 "300" 60(60)240, format(%02.0f) ang(h)) /// > xtitle("Fitted crying time (minutes)") || /// > lowess r fitted if(day>1), by(group, legend(off)) . graph export Figures/FigureDa-ResidualHeteroskedasticity.pdf, replace // Plots residuals depending of crying time (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureDa-ResidualHeteroskedasticity.pdf written in PDF format) . . * 2.6 Verifying normality of distribution of residuals . hist r, by(group) scheme(lean2) normal . graph export Figures/FigureE-DistResiduals.pdf, replace (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureE-DistResiduals.pdf written in PDF format) . drop r fitted . . * 2.7 Heteroskedasticity for log transformed data . mixed ct_ln group baseline_ln age_cat expectation2 day if(day>1) || id:, var reml // Models effects Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -617.44311 Iteration 1: log restricted-likelihood = -617.44311 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(5) = 125.85 Log restricted-likelihood = -617.44311 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ ct_ln | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -.0282336 .1288626 -0.22 0.827 -.2807995 .2243324 baseline_ln | .609172 .1312342 4.64 0.000 .3519576 .8663863 age_cat | -.2386402 .0654897 -3.64 0.000 -.3669976 -.1102828 expectation2 | .0295362 .0674373 0.44 0.661 -.1026385 .1617109 day | -.0518312 .0055272 -9.38 0.000 -.0626644 -.040998 _cons | 2.210387 .6798479 3.25 0.001 .8779093 3.542864 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | .1995745 .0447015 .1286619 .3095711 -----------------------------+------------------------------------------------ var(Residual) | .2870769 .0161652 .2570795 .3205745 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 218.10 Prob >= chibar2 = 0.0000 . predict r if(day>1), residuals (318 missing values generated) . predict fitted if(day>1) (option xb assumed) (147 missing values generated) . twoway /// > scatter r fitted if(day>1), by(group) scheme(lean2) mcolor(%30) /// > xla(2.875 "15" 3.488 "30" 4.139 "60" 4.81 "120" 5.492 "240" 6.047 "480", angle(90)) /// > xtitle("Observed crying time (minutes)") /// > ytitle("Residuals (ln(minutes))") || /// > lowess r fitted if(day>1), by(group, legend(off)) . graph export Figures/FigureDb-LnHeteroskadasticity.pdf, replace // Plots residuals depending of crying time (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureDb-LnHeteroskadasticity.pdf written in PDF format) . . . * 2.7 Analysing trends (https://stats.oarc.ucla.edu/stata/seminars/repeated-measures-analysis-with-stata/) . fp , scale: reg crying_time if(group==0 & day>1) (fitting 44 models) (....10%....20%....30%....40%....50%....60%....70%....80%....90%....100%) Fractional polynomial comparisons: ------------------------------------------------------------------------------- day | df Deviance Res. s.d. Dev. dif. P(*) Powers -------------+----------------------------------------------------------------- omitted | 0 4174.602 71.514 21.871 0.000 linear | 1 4153.337 69.567 0.606 0.897 1 m = 1 | 2 4152.999 69.535 0.269 0.876 .5 m = 2 | 4 4152.731 69.605 0.000 -- -.5 3 ------------------------------------------------------------------------------- (*) P = sig. level of model with m = 2 based on F with 362 denominator dof. Source | SS df MS Number of obs = 367 -------------+---------------------------------- F(2, 364) = 11.18 Model | 108288.86 2 54144.4298 Prob > F = 0.0000 Residual | 1763529.46 364 4844.86114 R-squared = 0.0579 -------------+---------------------------------- Adj R-squared = 0.0527 Total | 1871818.32 366 5114.25769 Root MSE = 69.605 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- day_1 | 27.9978 13.61399 2.06 0.040 1.225843 54.76975 day_2 | -8.209179 6.343413 -1.29 0.196 -20.68352 4.265159 _cons | 86.99493 21.71063 4.01 0.000 44.30093 129.6889 ------------------------------------------------------------------------------ . fp plot, scheme(lean2) residuals(none) . graph export Figures/FigureFa-TrendControl.pdf, replace (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureFa-TrendControl.pdf written in PDF format) . drop day_1 day_2 . . . fp , scale: reg crying_time if(group==1 & day>1) (fitting 44 models) (....10%....20%....30%....40%....50%....60%....70%....80%....90%....100%) Fractional polynomial comparisons: ------------------------------------------------------------------------------- day | df Deviance Res. s.d. Dev. dif. P(*) Powers -------------+----------------------------------------------------------------- omitted | 0 3775.671 88.421 10.419 0.036 linear | 1 3765.543 87.170 0.292 0.962 1 m = 1 | 2 3765.543 87.170 0.292 0.866 1 m = 2 | 4 3765.252 87.267 0.000 -- -1 -.5 ------------------------------------------------------------------------------- (*) P = sig. level of model with m = 2 based on F with 315 denominator dof. Source | SS df MS Number of obs = 320 -------------+---------------------------------- F(2, 317) = 5.25 Model | 79899.1825 2 39949.5913 Prob > F = 0.0057 Residual | 2414143.06 317 7615.59326 R-squared = 0.0320 -------------+---------------------------------- Adj R-squared = 0.0259 Total | 2494042.25 319 7818.31425 Root MSE = 87.267 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- day_1 | -44.24972 29.62628 -1.49 0.136 -102.5387 14.03927 day_2 | 167.1678 89.13584 1.88 0.062 -8.204776 342.5404 _cons | -7.429278 61.67689 -0.12 0.904 -128.7771 113.9185 ------------------------------------------------------------------------------ . fp plot, scheme(lean2) residuals(none) . drop day_1 day_2 . graph export Figures/FigureFb-TrendTest.pdf, replace (file /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/Figures/FigureFb-TrendTest.pdf written in PDF format) . . mixed crying_time group##day if(day>1)|| id:, var reml // Models effects Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3697.5268 Iteration 1: log restricted-likelihood = -3697.5268 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(25) = 74.14 Log restricted-likelihood = -3697.5268 Prob > chi2 = 0.0000 -------------------------------------------------------------------------------------- crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---------------------+---------------------------------------------------------------- group | Test group (TTR) | -2.242812 21.50821 -0.10 0.917 -44.39813 39.91251 | day | 3 | -7.482759 14.38609 -0.52 0.603 -35.67898 20.71346 4 | -13.82759 14.38609 -0.96 0.336 -42.0238 14.36863 5 | -32.85615 14.52681 -2.26 0.024 -61.32817 -4.384135 6 | -23.85622 14.67502 -1.63 0.104 -52.61874 4.906302 7 | -37.27586 14.38609 -2.59 0.010 -65.47208 -9.079644 8 | -32.37931 14.38609 -2.25 0.024 -60.57553 -4.183093 9 | -43.7552 14.52564 -3.01 0.003 -72.22492 -15.28547 10 | -33.86234 14.52564 -2.33 0.020 -62.33207 -5.392617 11 | -44.24138 14.38609 -3.08 0.002 -72.4376 -16.04516 12 | -51.14656 14.67512 -3.49 0.000 -79.90925 -22.38386 13 | -55.18359 14.67512 -3.76 0.000 -83.94629 -26.4209 14 | -59.17803 14.52563 -4.07 0.000 -87.64775 -30.70831 | group#day | Test group (TTR)# 3 | -4.082614 21.27023 -0.19 0.848 -45.77149 37.60627 Test group (TTR)# 4 | 27.93341 21.27595 1.31 0.189 -13.76669 69.6335 Test group (TTR)# 5 | 31.84963 21.14021 1.51 0.132 -9.584428 73.28368 Test group (TTR)# 6 | -5.996463 21.24234 -0.28 0.778 -47.63067 35.63775 Test group (TTR)# 7 | 20.81066 21.16826 0.98 0.326 -20.67837 62.2997 Test group (TTR)# 8 | 23.51411 21.16826 1.11 0.267 -17.97492 65.00315 Test group (TTR)# 9 | 8.940981 21.13941 0.42 0.672 -32.4915 50.37346 Test group (TTR)#10 | -7.685645 21.39484 -0.36 0.719 -49.61877 34.24748 Test group (TTR)#11 | 4.173723 21.16666 0.20 0.844 -37.31218 45.65962 Test group (TTR)#12 | 10.9319 21.49661 0.51 0.611 -31.20068 53.06449 Test group (TTR)#13 | 20.35594 21.36416 0.95 0.341 -21.51704 62.22891 Test group (TTR)#14 | 11.77635 21.8434 0.54 0.590 -31.03592 54.58861 | _cons | 148.1724 14.70721 10.07 0.000 119.3468 176.998 -------------------------------------------------------------------------------------- ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3271.848 683.5586 2172.496 4927.507 -----------------------------+------------------------------------------------ var(Residual) | 3000.914 172.1227 2681.831 3357.961 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 343.94 Prob >= chibar2 = 0.0000 . contrast group##day // Tests whether effects change over time Contrasts of marginal linear predictions Margins : asbalanced ------------------------------------------------ | df chi2 P>chi2 -------------+---------------------------------- crying_time | group | 1 0.30 0.5861 | day | 12 64.39 0.0000 | group#day | 12 9.24 0.6823 ------------------------------------------------ . margins group#day // Measure day to day means in each group using Delta-method linear prediction with fixed portion Adjusted predictions Number of obs = 687 Expression : Linear prediction, fixed portion, predict() ----------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ------------------------+---------------------------------------------------------------- group#day | Control group (GTR)# 2 | 148.1724 14.70721 10.07 0.000 119.3468 176.998 Control group (GTR)# 3 | 140.6897 14.70721 9.57 0.000 111.864 169.5153 Control group (GTR)# 4 | 134.3448 14.70721 9.13 0.000 105.5192 163.1704 Control group (GTR)# 5 | 115.3163 14.84489 7.77 0.000 86.22082 144.4117 Control group (GTR)# 6 | 124.3162 14.98996 8.29 0.000 94.93642 153.696 Control group (GTR)# 7 | 110.8966 14.70721 7.54 0.000 82.07094 139.7222 Control group (GTR)# 8 | 115.7931 14.70721 7.87 0.000 86.96749 144.6187 Control group (GTR)# 9 | 104.4172 14.84374 7.03 0.000 75.32402 133.5104 Control group (GTR)#10 | 114.3101 14.84374 7.70 0.000 85.21687 143.4033 Control group (GTR)#11 | 103.931 14.70721 7.07 0.000 75.10543 132.7566 Control group (GTR)#12 | 97.02586 14.99005 6.47 0.000 67.6459 126.4058 Control group (GTR)#13 | 92.98882 14.99005 6.20 0.000 63.60886 122.3688 Control group (GTR)#14 | 88.99439 14.84374 6.00 0.000 59.90119 118.0876 Test group (TTR)# 2 | 145.9296 15.69398 9.30 0.000 115.17 176.6892 Test group (TTR)# 3 | 134.3642 15.86484 8.47 0.000 103.2697 165.4588 Test group (TTR)# 4 | 160.0354 15.87176 10.08 0.000 128.9273 191.1435 Test group (TTR)# 5 | 144.9231 15.53255 9.33 0.000 114.4798 175.3663 Test group (TTR)# 6 | 116.0769 15.53255 7.47 0.000 85.63369 146.5202 Test group (TTR)# 7 | 129.4644 15.69907 8.25 0.000 98.6948 160.234 Test group (TTR)# 8 | 137.0644 15.69907 8.73 0.000 106.2948 167.834 Test group (TTR)# 9 | 111.1154 15.53255 7.15 0.000 80.67215 141.5586 Test group (TTR)#10 | 104.3816 15.87493 6.58 0.000 73.26732 135.4959 Test group (TTR)#11 | 105.8619 15.69699 6.74 0.000 75.09641 136.6275 Test group (TTR)#12 | 105.7149 15.87493 6.66 0.000 74.60066 136.8292 Test group (TTR)#13 | 111.1019 15.69699 7.08 0.000 80.33641 141.8675 Test group (TTR)#14 | 98.52792 16.47018 5.98 0.000 66.24696 130.8089 ----------------------------------------------------------------------------------------- . . contrast day@group, effect // Test of simple effects, Contrasts of marginal linear predictions Contrasts of marginal linear predictions Margins : asbalanced -------------------------------------------------------- | df chi2 P>chi2 ---------------------+---------------------------------- crying_time | day@group | Control group (GTR) | 12 37.24 0.0002 Test group (TTR) | 12 36.60 0.0003 Joint | 24 73.84 0.0000 -------------------------------------------------------- --------------------------------------------------------------------------------------------------- | Contrast Std. Err. z P>|z| [95% Conf. Interval] ----------------------------------+---------------------------------------------------------------- crying_time | day@group | (3 vs base) Control group (GTR) | -7.482759 14.38609 -0.52 0.603 -35.67898 20.71346 (3 vs base) Test group (TTR) | -11.56537 15.66726 -0.74 0.460 -42.27264 19.14189 (4 vs base) Control group (GTR) | -13.82759 14.38609 -0.96 0.336 -42.0238 14.36863 (4 vs base) Test group (TTR) | 14.10582 15.67503 0.90 0.368 -16.61667 44.82831 (5 vs base) Control group (GTR) | -32.85615 14.52681 -2.26 0.024 -61.32817 -4.384135 (5 vs base) Test group (TTR) | -1.006525 15.3584 -0.07 0.948 -31.10844 29.09539 (6 vs base) Control group (GTR) | -23.85622 14.67502 -1.63 0.104 -52.61874 4.906302 (6 vs base) Test group (TTR) | -29.85268 15.3584 -1.94 0.052 -59.95459 .2492332 (7 vs base) Control group (GTR) | -37.27586 14.38609 -2.59 0.010 -65.47208 -9.079644 (7 vs base) Test group (TTR) | -16.4652 15.52855 -1.06 0.289 -46.90059 13.9702 (8 vs base) Control group (GTR) | -32.37931 14.38609 -2.25 0.024 -60.57553 -4.183093 (8 vs base) Test group (TTR) | -8.865198 15.52855 -0.57 0.568 -39.30059 21.5702 (9 vs base) Control group (GTR) | -43.7552 14.52564 -3.01 0.003 -72.22492 -15.28547 (9 vs base) Test group (TTR) | -34.81422 15.3584 -2.27 0.023 -64.91613 -4.712305 (10 vs base) Control group (GTR) | -33.86234 14.52564 -2.33 0.020 -62.33207 -5.392617 (10 vs base) Test group (TTR) | -41.54799 15.70813 -2.64 0.008 -72.33535 -10.76062 (11 vs base) Control group (GTR) | -44.24138 14.38609 -3.08 0.002 -72.4376 -16.04516 (11 vs base) Test group (TTR) | -40.06766 15.52637 -2.58 0.010 -70.49878 -9.636533 (12 vs base) Control group (GTR) | -51.14656 14.67512 -3.49 0.000 -79.90925 -22.38386 (12 vs base) Test group (TTR) | -40.21465 15.70813 -2.56 0.010 -71.00202 -9.42729 (13 vs base) Control group (GTR) | -55.18359 14.67512 -3.76 0.000 -83.94629 -26.4209 (13 vs base) Test group (TTR) | -34.82766 15.52637 -2.24 0.025 -65.25878 -4.396533 (14 vs base) Control group (GTR) | -59.17803 14.52563 -4.07 0.000 -87.64775 -30.70831 (14 vs base) Test group (TTR) | -47.40168 16.31379 -2.91 0.004 -79.37613 -15.42723 --------------------------------------------------------------------------------------------------- . margins day, at(group=0) pwcompare(effects) // Changes over time in control group (GTR) Pairwise comparisons of adjusted predictions Expression : Linear prediction, fixed portion, predict() at : group = 0 ------------------------------------------------------------------------------ | Delta-method Unadjusted Unadjusted | Contrast Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- day | 3 vs 2 | -7.482759 14.38609 -0.52 0.603 -35.67898 20.71346 4 vs 2 | -13.82759 14.38609 -0.96 0.336 -42.0238 14.36863 5 vs 2 | -32.85615 14.52681 -2.26 0.024 -61.32817 -4.384135 6 vs 2 | -23.85622 14.67502 -1.63 0.104 -52.61874 4.906302 7 vs 2 | -37.27586 14.38609 -2.59 0.010 -65.47208 -9.079644 8 vs 2 | -32.37931 14.38609 -2.25 0.024 -60.57553 -4.183093 9 vs 2 | -43.7552 14.52564 -3.01 0.003 -72.22492 -15.28547 10 vs 2 | -33.86234 14.52564 -2.33 0.020 -62.33207 -5.392617 11 vs 2 | -44.24138 14.38609 -3.08 0.002 -72.4376 -16.04516 12 vs 2 | -51.14656 14.67512 -3.49 0.000 -79.90925 -22.38386 13 vs 2 | -55.18359 14.67512 -3.76 0.000 -83.94629 -26.4209 14 vs 2 | -59.17803 14.52563 -4.07 0.000 -87.64775 -30.70831 4 vs 3 | -6.344828 14.38609 -0.44 0.659 -34.54105 21.85139 5 vs 3 | -25.37339 14.52681 -1.75 0.081 -53.84541 3.098623 6 vs 3 | -16.37346 14.67502 -1.12 0.265 -45.13598 12.38906 7 vs 3 | -29.7931 14.38609 -2.07 0.038 -57.98932 -1.596886 8 vs 3 | -24.89655 14.38609 -1.73 0.084 -53.09277 3.299666 9 vs 3 | -36.27244 14.52564 -2.50 0.013 -64.74217 -7.802716 10 vs 3 | -26.37958 14.52564 -1.82 0.069 -54.84931 2.090141 11 vs 3 | -36.75862 14.38609 -2.56 0.011 -64.95484 -8.562403 12 vs 3 | -43.6638 14.67512 -2.98 0.003 -72.4265 -14.9011 13 vs 3 | -47.70083 14.67512 -3.25 0.001 -76.46353 -18.93814 14 vs 3 | -51.69527 14.52563 -3.56 0.000 -80.16499 -23.22555 5 vs 4 | -19.02857 14.52681 -1.31 0.190 -47.50058 9.443451 6 vs 4 | -10.02863 14.67502 -0.68 0.494 -38.79115 18.73389 7 vs 4 | -23.44828 14.38609 -1.63 0.103 -51.64449 4.747942 8 vs 4 | -18.55172 14.38609 -1.29 0.197 -46.74794 9.644494 9 vs 4 | -29.92761 14.52564 -2.06 0.039 -58.39734 -1.457888 10 vs 4 | -20.03476 14.52564 -1.38 0.168 -48.50448 8.434969 11 vs 4 | -30.41379 14.38609 -2.11 0.035 -58.61001 -2.217575 12 vs 4 | -37.31897 14.67512 -2.54 0.011 -66.08167 -8.556272 13 vs 4 | -41.35601 14.67512 -2.82 0.005 -70.1187 -12.59331 14 vs 4 | -45.35044 14.52563 -3.12 0.002 -73.82016 -16.88072 6 vs 5 | 8.999936 14.78797 0.61 0.543 -19.98396 37.98383 7 vs 5 | -4.419709 14.52681 -0.30 0.761 -32.89173 24.05231 8 vs 5 | .4768424 14.52681 0.03 0.974 -27.99518 28.94886 9 vs 5 | -10.89905 14.66608 -0.74 0.457 -39.64404 17.84595 10 vs 5 | -1.006189 14.66608 -0.07 0.945 -29.75119 27.73881 11 vs 5 | -11.38523 14.52681 -0.78 0.433 -39.85724 17.08679 12 vs 5 | -18.2904 14.78797 -1.24 0.216 -47.2743 10.69349 13 vs 5 | -22.32744 14.78797 -1.51 0.131 -51.31134 6.656457 14 vs 5 | -26.32187 14.666 -1.79 0.073 -55.0667 2.422949 7 vs 6 | -13.41965 14.67502 -0.91 0.360 -42.18216 15.34287 8 vs 6 | -8.523094 14.67502 -0.58 0.561 -37.28561 20.23943 9 vs 6 | -19.89898 14.78921 -1.35 0.178 -48.8853 9.087332 10 vs 6 | -10.00613 14.78921 -0.68 0.499 -38.99244 18.98019 11 vs 6 | -20.38516 14.67502 -1.39 0.165 -49.14768 8.377356 12 vs 6 | -27.29034 14.93491 -1.83 0.068 -56.56223 1.981554 13 vs 6 | -31.32738 14.93491 -2.10 0.036 -60.59927 -2.055483 14 vs 6 | -35.32181 14.81393 -2.38 0.017 -64.35658 -6.287038 8 vs 7 | 4.896552 14.38609 0.34 0.734 -23.29967 33.09277 9 vs 7 | -6.479337 14.52564 -0.45 0.656 -34.94906 21.99039 10 vs 7 | 3.41352 14.52564 0.23 0.814 -25.0562 31.88324 11 vs 7 | -6.965517 14.38609 -0.48 0.628 -35.16173 21.2307 12 vs 7 | -13.87069 14.67512 -0.95 0.345 -42.63339 14.892 13 vs 7 | -17.90773 14.67512 -1.22 0.222 -46.67043 10.85497 14 vs 7 | -21.90216 14.52563 -1.51 0.132 -50.37188 6.567556 9 vs 8 | -11.37589 14.52564 -0.78 0.434 -39.84561 17.09384 10 vs 8 | -1.483032 14.52564 -0.10 0.919 -29.95276 26.98669 11 vs 8 | -11.86207 14.38609 -0.82 0.410 -40.05829 16.33415 12 vs 8 | -18.76725 14.67512 -1.28 0.201 -47.52994 9.995452 13 vs 8 | -22.80428 14.67512 -1.55 0.120 -51.56698 5.958415 14 vs 8 | -26.79872 14.52563 -1.84 0.065 -55.26844 1.671004 10 vs 9 | 9.892857 14.64073 0.68 0.499 -18.80245 38.58816 11 vs 9 | -.4861803 14.52564 -0.03 0.973 -28.9559 27.98354 12 vs 9 | -7.391357 14.81411 -0.50 0.618 -36.42649 21.64377 13 vs 9 | -11.42839 14.81411 -0.77 0.440 -40.46352 17.60673 14 vs 9 | -15.42283 14.6649 -1.05 0.293 -44.1655 13.31985 11 vs 10 | -10.37904 14.52564 -0.71 0.475 -38.84876 18.09069 12 vs 10 | -17.28421 14.81411 -1.17 0.243 -46.31934 11.75091 13 vs 10 | -21.32125 14.81411 -1.44 0.150 -50.35638 7.713877 14 vs 10 | -25.31568 14.6649 -1.73 0.084 -54.05836 3.426992 12 vs 11 | -6.905177 14.67512 -0.47 0.638 -35.66787 21.85752 13 vs 11 | -10.94221 14.67512 -0.75 0.456 -39.70491 17.82048 14 vs 11 | -14.93665 14.52563 -1.03 0.304 -43.40637 13.53307 13 vs 12 | -4.037037 14.90939 -0.27 0.787 -33.2589 25.18483 14 vs 12 | -8.03147 14.78921 -0.54 0.587 -37.01779 20.95485 14 vs 13 | -3.994433 14.78921 -0.27 0.787 -32.98075 24.99189 ------------------------------------------------------------------------------ . margins day, at(group=1) pwcompare(effects) // Changes over time in test group (TTR) Pairwise comparisons of adjusted predictions Expression : Linear prediction, fixed portion, predict() at : group = 1 ------------------------------------------------------------------------------ | Delta-method Unadjusted Unadjusted | Contrast Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- day | 3 vs 2 | -11.56537 15.66726 -0.74 0.460 -42.27264 19.14189 4 vs 2 | 14.10582 15.67503 0.90 0.368 -16.61667 44.82831 5 vs 2 | -1.006525 15.3584 -0.07 0.948 -31.10844 29.09539 6 vs 2 | -29.85268 15.3584 -1.94 0.052 -59.95459 .2492332 7 vs 2 | -16.4652 15.52855 -1.06 0.289 -46.90059 13.9702 8 vs 2 | -8.865198 15.52855 -0.57 0.568 -39.30059 21.5702 9 vs 2 | -34.81422 15.3584 -2.27 0.023 -64.91613 -4.712305 10 vs 2 | -41.54799 15.70813 -2.64 0.008 -72.33535 -10.76062 11 vs 2 | -40.06766 15.52637 -2.58 0.010 -70.49878 -9.636533 12 vs 2 | -40.21465 15.70813 -2.56 0.010 -71.00202 -9.42729 13 vs 2 | -34.82766 15.52637 -2.24 0.025 -65.25878 -4.396533 14 vs 2 | -47.40168 16.31379 -2.91 0.004 -79.37613 -15.42723 4 vs 3 | 25.67119 15.84779 1.62 0.105 -5.389911 56.7323 5 vs 3 | 10.55885 15.53296 0.68 0.497 -19.88519 41.00288 6 vs 3 | -18.28731 15.53296 -1.18 0.239 -48.73134 12.15673 7 vs 3 | -4.899826 15.70293 -0.31 0.755 -35.67701 25.87736 8 vs 3 | 2.700174 15.70293 0.17 0.863 -28.07701 33.47736 9 vs 3 | -23.24885 15.53296 -1.50 0.134 -53.69288 7.195191 10 vs 3 | -29.98261 15.88217 -1.89 0.059 -61.1111 1.145873 11 vs 3 | -28.50228 15.70057 -1.82 0.069 -59.27484 2.270277 12 vs 3 | -28.64928 15.88217 -1.80 0.071 -59.77777 2.479207 13 vs 3 | -23.26228 15.70057 -1.48 0.138 -54.03484 7.510277 14 vs 3 | -35.83631 16.48536 -2.17 0.030 -68.14703 -3.525587 5 vs 4 | -15.11235 15.54002 -0.97 0.331 -45.57023 15.34553 6 vs 4 | -43.9585 15.54002 -2.83 0.005 -74.41638 -13.50062 7 vs 4 | -30.57102 15.66951 -1.95 0.051 -61.28269 .140654 8 vs 4 | -22.97102 15.66951 -1.47 0.143 -53.68269 7.740654 9 vs 4 | -48.92004 15.54002 -3.15 0.002 -79.37792 -18.46216 10 vs 4 | -55.65381 15.8479 -3.51 0.000 -86.71511 -24.5925 11 vs 4 | -54.17348 15.70798 -3.45 0.001 -84.96056 -23.3864 12 vs 4 | -54.32047 15.8479 -3.43 0.001 -85.38178 -23.25917 13 vs 4 | -48.93348 15.70798 -3.12 0.002 -79.72056 -18.1464 14 vs 4 | -61.5075 16.44853 -3.74 0.000 -93.74603 -29.26897 6 vs 5 | -28.84615 15.1934 -1.90 0.058 -58.62468 .932371 7 vs 5 | -15.45867 15.3636 -1.01 0.314 -45.57077 14.65343 8 vs 5 | -7.858673 15.3636 -0.51 0.609 -37.97077 22.25343 9 vs 5 | -33.80769 15.1934 -2.23 0.026 -63.58622 -4.029167 10 vs 5 | -40.54146 15.54326 -2.61 0.009 -71.00569 -10.07723 11 vs 5 | -39.06113 15.36147 -2.54 0.011 -69.16907 -8.953193 12 vs 5 | -39.20813 15.54326 -2.52 0.012 -69.67236 -8.743901 13 vs 5 | -33.82113 15.36147 -2.20 0.028 -63.92907 -3.713193 14 vs 5 | -46.39516 16.15074 -2.87 0.004 -78.05002 -14.74029 7 vs 6 | 13.38748 15.3636 0.87 0.384 -16.72462 43.49958 8 vs 6 | 20.98748 15.3636 1.37 0.172 -9.124618 51.09958 9 vs 6 | -4.961538 15.1934 -0.33 0.744 -34.74006 24.81699 10 vs 6 | -11.69531 15.54326 -0.75 0.452 -42.15953 18.76892 11 vs 6 | -10.21498 15.36147 -0.66 0.506 -40.32291 19.89296 12 vs 6 | -10.36197 15.54326 -0.67 0.505 -40.8262 20.10225 13 vs 6 | -4.974977 15.36147 -0.32 0.746 -35.08291 25.13296 14 vs 6 | -17.549 16.15074 -1.09 0.277 -49.20387 14.10586 8 vs 7 | 7.6 15.49429 0.49 0.624 -22.76826 37.96826 9 vs 7 | -18.34902 15.3636 -1.19 0.232 -48.46112 11.76308 10 vs 7 | -25.08279 15.67278 -1.60 0.110 -55.80087 5.635287 11 vs 7 | -23.60246 15.53164 -1.52 0.129 -54.04392 6.839 12 vs 7 | -23.74946 15.67278 -1.52 0.130 -54.46753 6.968621 13 vs 7 | -18.36246 15.53164 -1.18 0.237 -48.80392 12.079 14 vs 7 | -30.93648 16.27536 -1.90 0.057 -62.8356 .9626354 9 vs 8 | -25.94902 15.3636 -1.69 0.091 -56.06112 4.16308 10 vs 8 | -32.68279 15.67278 -2.09 0.037 -63.40087 -1.964713 11 vs 8 | -31.20246 15.53164 -2.01 0.045 -61.64392 -.7609998 12 vs 8 | -31.34946 15.67278 -2.00 0.045 -62.06753 -.6313795 13 vs 8 | -25.96246 15.53164 -1.67 0.095 -56.40392 4.479 14 vs 8 | -38.53648 16.27536 -2.37 0.018 -70.4356 -6.637365 10 vs 9 | -6.733769 15.54326 -0.43 0.665 -37.198 23.73046 11 vs 9 | -5.253438 15.36147 -0.34 0.732 -35.36138 24.8545 12 vs 9 | -5.400436 15.54326 -0.35 0.728 -35.86466 25.06379 13 vs 9 | -.0134384 15.36147 -0.00 0.999 -30.12138 30.0945 14 vs 9 | -12.58746 16.15074 -0.78 0.436 -44.24233 19.0674 11 vs 10 | 1.480331 15.67503 0.09 0.925 -29.24216 32.20283 12 vs 10 | 1.333333 15.8138 0.08 0.933 -29.66114 32.3278 13 vs 10 | 6.720331 15.67503 0.43 0.668 -24.00216 37.44283 14 vs 10 | -5.853693 16.41112 -0.36 0.721 -38.0189 26.31151 12 vs 11 | -.1469977 15.67503 -0.01 0.993 -30.86949 30.5755 13 vs 11 | 5.24 15.49429 0.34 0.735 -25.12826 35.60826 14 vs 11 | -7.334024 16.27752 -0.45 0.652 -39.23737 24.56932 13 vs 12 | 5.386998 15.67503 0.34 0.731 -25.3355 36.10949 14 vs 12 | -7.187027 16.41112 -0.44 0.661 -39.35223 24.97818 14 vs 13 | -12.57402 16.27752 -0.77 0.440 -44.47737 19.32932 ------------------------------------------------------------------------------ . contrast group@day, effect // Effects between group over time Contrasts of marginal linear predictions Margins : asbalanced ------------------------------------------------ | df chi2 P>chi2 -------------+---------------------------------- crying_time | group@day | 2 | 1 0.01 0.9169 3 | 1 0.09 0.7700 4 | 1 1.41 0.2351 5 | 1 1.90 0.1682 6 | 1 0.15 0.7027 7 | 1 0.75 0.3881 8 | 1 0.98 0.3228 9 | 1 0.10 0.7552 10 | 1 0.21 0.6478 11 | 1 0.01 0.9285 12 | 1 0.16 0.6907 13 | 1 0.70 0.4040 14 | 1 0.18 0.6672 Joint | 13 9.54 0.7311 ------------------------------------------------ ------------------------------------------------------------------------------------------------ | Contrast Std. Err. z P>|z| [95% Conf. Interval] -------------------------------+---------------------------------------------------------------- crying_time | group@day | (Test group (TTR) vs base) 2 | -2.242812 21.50821 -0.10 0.917 -44.39813 39.91251 (Test group (TTR) vs base) 3 | -6.325425 21.6332 -0.29 0.770 -48.72572 36.07487 (Test group (TTR) vs base) 4 | 25.6906 21.63827 1.19 0.235 -16.71964 68.10083 (Test group (TTR) vs base) 5 | 29.60682 21.48559 1.38 0.168 -12.50417 71.71781 (Test group (TTR) vs base) 6 | -8.239274 21.58608 -0.38 0.703 -50.54722 34.06867 (Test group (TTR) vs base) 7 | 18.56785 21.51192 0.86 0.388 -23.59474 60.73045 (Test group (TTR) vs base) 8 | 21.2713 21.51192 0.99 0.323 -20.89129 63.4339 (Test group (TTR) vs base) 9 | 6.69817 21.4848 0.31 0.755 -35.41127 48.80761 (Test group (TTR) vs base) 10 | -9.928457 21.73362 -0.46 0.648 -52.52556 32.66865 (Test group (TTR) vs base) 11 | 1.930912 21.51041 0.09 0.928 -40.22871 44.09053 (Test group (TTR) vs base) 12 | 8.689091 21.8338 0.40 0.691 -34.10438 51.48256 (Test group (TTR) vs base) 13 | 18.11313 21.70477 0.83 0.404 -24.42744 60.65369 (Test group (TTR) vs base) 14 | 9.533535 22.17213 0.43 0.667 -33.92305 52.99012 ------------------------------------------------------------------------------------------------ . . * Post-hoc test of trends . contrast p.day@group, effect Contrasts of marginal linear predictions Margins : asbalanced -------------------------------------------------------------------- | df chi2 P>chi2 ---------------------------------+---------------------------------- crying_time | day@group | (linear) Control group (GTR) | 1 33.20 0.0000 (linear) Test group (TTR) | 1 25.85 0.0000 (quadratic) Control group (GTR) | 1 0.57 0.4502 (quadratic) Test group (TTR) | 1 0.04 0.8349 (cubic) Control group (GTR) | 1 0.95 0.3304 (cubic) Test group (TTR) | 1 1.03 0.3093 (quartic) Control group (GTR) | 1 0.00 0.9593 (quartic) Test group (TTR) | 1 0.35 0.5557 (quintic) Control group (GTR) | 1 0.28 0.5992 (quintic) Test group (TTR) | 1 0.21 0.6483 (sextic) Control group (GTR) | 1 0.00 0.9713 (sextic) Test group (TTR) | 1 0.25 0.6171 (septic) Control group (GTR) | 1 0.02 0.8938 (septic) Test group (TTR) | 1 1.95 0.1627 (octic) Control group (GTR) | 1 0.06 0.8043 (octic) Test group (TTR) | 1 4.17 0.0411 (nonic) Control group (GTR) | 1 0.34 0.5592 (nonic) Test group (TTR) | 1 0.86 0.3544 (degree 10) Control group (GTR) | 1 0.32 0.5723 (degree 10) Test group (TTR) | 1 1.18 0.2772 (degree 11) Control group (GTR) | 1 0.05 0.8289 (degree 11) Test group (TTR) | 1 0.47 0.4908 (degree 12) Control group (GTR) | 1 1.27 0.2592 (degree 12) Test group (TTR) | 1 0.07 0.7848 Joint | 24 73.84 0.0000 -------------------------------------------------------------------- -------------------------------------------------------------------------------------------------- | Contrast Std. Err. z P>|z| [95% Conf. Interval] ---------------------------------+---------------------------------------------------------------- crying_time | day@group | (linear) Control group (GTR) | -16.51871 2.866964 -5.76 0.000 -22.13785 -10.89956 (linear) Test group (TTR) | -15.97249 3.141812 -5.08 0.000 -22.13033 -9.814647 (quadratic) Control group (GTR) | 2.15832 2.858156 0.76 0.450 -3.443564 7.760204 (quadratic) Test group (TTR) | .6492749 3.115025 0.21 0.835 -5.456063 6.754612 (cubic) Control group (GTR) | -2.78321 2.859663 -0.97 0.330 -8.388047 2.821628 (cubic) Test group (TTR) | 3.157249 3.105583 1.02 0.309 -2.929582 9.24408 (quartic) Control group (GTR) | .1460482 2.858858 0.05 0.959 -5.45721 5.749306 (quartic) Test group (TTR) | -1.826694 3.099961 -0.59 0.556 -7.902505 4.249118 (quintic) Control group (GTR) | 1.512443 2.877491 0.53 0.599 -4.127336 7.152222 (quintic) Test group (TTR) | -1.403761 3.077943 -0.46 0.648 -7.436419 4.628896 (sextic) Control group (GTR) | -.1029911 2.85932 -0.04 0.971 -5.707155 5.501173 (sextic) Test group (TTR) | -1.528268 3.056509 -0.50 0.617 -7.518916 4.46238 (septic) Control group (GTR) | .3828146 2.866268 0.13 0.894 -5.234967 6.000596 (septic) Test group (TTR) | -4.269712 3.05822 -1.40 0.163 -10.26371 1.724289 (octic) Control group (GTR) | -.7124534 2.875107 -0.25 0.804 -6.34756 4.922653 (octic) Test group (TTR) | 6.289809 3.079956 2.04 0.041 .2532066 12.32641 (nonic) Control group (GTR) | -1.670696 2.860753 -0.58 0.559 -7.277669 3.936278 (nonic) Test group (TTR) | -2.820796 3.046092 -0.93 0.354 -8.791027 3.149435 (degree 10) Control group (GTR) | 1.615781 2.861418 0.56 0.572 -3.992496 7.224058 (degree 10) Test group (TTR) | -3.301668 3.038713 -1.09 0.277 -9.257436 2.654101 (degree 11) Control group (GTR) | -.6208591 2.872051 -0.22 0.829 -6.249975 5.008257 (degree 11) Test group (TTR) | 2.090262 3.033607 0.69 0.491 -3.855499 8.036023 (degree 12) Control group (GTR) | 3.213551 2.848309 1.13 0.259 -2.369032 8.796134 (degree 12) Test group (TTR) | .8265993 3.026617 0.27 0.785 -5.105461 6.75866 -------------------------------------------------------------------------------------------------- . . * Post-hoc test of partial interaction . contrast a.day#group // Contrasts of marginal linear predictions Margins : asbalanced ------------------------------------------------------- | df chi2 P>chi2 --------------------+---------------------------------- crying_time | day#group | (2 vs 3) (joint) | 1 0.04 0.8478 (3 vs 4) (joint) | 1 2.24 0.1347 (4 vs 5) (joint) | 1 0.03 0.8539 (5 vs 6) (joint) | 1 3.19 0.0743 (6 vs 7) (joint) | 1 1.59 0.2070 (7 vs 8) (joint) | 1 0.02 0.8983 (8 vs 9) (joint) | 1 0.48 0.4907 (9 vs 10) (joint) | 1 0.61 0.4362 (10 vs 11) (joint) | 1 0.31 0.5789 (11 vs 12) (joint) | 1 0.10 0.7530 (12 vs 13) (joint) | 1 0.19 0.6631 (13 vs 14) (joint) | 1 0.15 0.6965 Joint | 12 9.24 0.6823 ------------------------------------------------------- . . . . * ---------------------- 3. DESCRIPTIVE STATISTICS ------------------ . . * 3.1 Missing data . * 3.1.1 Full dataset (long) . mdesc // describing missing data Variable | Missing Total Percent Missing ----------------+----------------------------------------------- id | 0 1,005 0.00 day | 0 1,005 0.00 pract | 660 1,005 65.67 group | 15 1,005 1.49 allocation~s | 15 1,005 1.49 perprotocol | 15 1,005 1.49 date_b | 15 1,005 1.49 date_end | 120 1,005 11.94 days | 120 1,005 11.94 visits | 45 1,005 4.48 parent | 15 1,005 1.49 gender | 15 1,005 1.49 age | 15 1,005 1.49 age_cat | 15 1,005 1.49 bw | 15 1,005 1.49 weight_b | 75 1,005 7.46 weight_end | 150 1,005 14.93 weight_cha~e | 210 1,005 20.90 ct_b | 45 1,005 4.48 sibling | 15 1,005 1.49 parent_age | 15 1,005 1.49 parent_type | 15 1,005 1.49 expectation | 15 1,005 1.49 crying_time | 203 1,005 20.20 pcs_b | 30 1,005 2.99 pcs_e | 150 1,005 14.93 pcs_cat | 15 1,005 1.49 help_belie~d | 15 1,005 1.49 experience~d | 15 1,005 1.49 experience~g | 15 1,005 1.49 satisfacti~d | 15 1,005 1.49 gc_end | 15 1,005 1.49 ur_reported | 15 1,005 1.49 ur_type1 | 15 1,005 1.49 ur_type2 | 15 1,005 1.49 ur_type3 | 15 1,005 1.49 ur_type4 | 15 1,005 1.49 ur_type5 | 15 1,005 1.49 ur_type6 | 15 1,005 1.49 ur_type7_txt | 600 1,005 59.70 ad_sleep | 0 1,005 0.00 ad_feeding | 0 1,005 0.00 ad_diet | 0 1,005 0.00 ad_handling | 0 1,005 0.00 ad_env | 0 1,005 0.00 ad_beh | 0 1,005 0.00 ad_reas | 0 1,005 0.00 ad_other | 0 1,005 0.00 other_care | 15 1,005 1.49 other_care1 | 15 1,005 1.49 other_care2 | 15 1,005 1.49 other_care3 | 15 1,005 1.49 other_care4 | 15 1,005 1.49 other_care5 | 15 1,005 1.49 other_care6 | 15 1,005 1.49 other_care7 | 15 1,005 1.49 other_care8 | 15 1,005 1.49 other_care~t | 720 1,005 71.64 pharm1 | 15 1,005 1.49 pharm2 | 15 1,005 1.49 pharm3 | 15 1,005 1.49 pharm4 | 15 1,005 1.49 pharm5 | 15 1,005 1.49 pharm6 | 15 1,005 1.49 pharm7 | 15 1,005 1.49 pharm7_txt | 675 1,005 67.16 ur_type7 | 15 1,005 1.49 inst | 15 1,005 1.49 origin_b | 0 1,005 0.00 baseline | 15 1,005 1.49 expectation2 | 15 1,005 1.49 ct_ln | 203 1,005 20.20 baseline_ln | 15 1,005 1.49 r | 318 1,005 31.64 fitted | 147 1,005 14.63 ----------------+----------------------------------------------- . . egen nmis=rowmiss(id inst day pract group allocation_guess perprotocol /// > visits age expectation baseline crying_time pcs_b pcs_e experience_end /// > satisfaction_end gc_end ur_reported ad_sleep ad_feeding ad_diet ad_handling /// > ad_env ad_beh other_care1 other_care2 other_care3 other_care4 other_care5 /// > other_care6 other_care7 pharm1 pharm2 pharm3 pharm4 pharm5 pharm6) . . mvpatterns id day group allocation_guess /// > visits age expectation baseline crying_time pcs_b pcs_e experience_end /// > satisfaction_end gc_end variables with no mv's: id day Variable | type obs mv variable label -------------+--------------------------------------------------------------- group | byte 990 15 Group allocation allocation_guess|byte 990 15 Group allocation guess visits | long 960 45 Number of sessions age | byte 990 15 Infant age (days) expectation | byte 990 15 Expectations baseline | float 990 15 Crying time day before treatment (minutes) crying_time | float 802 203 Daily crying time (minutes) pcs_b | float 975 30 Parenting Confidence Score at baseline pcs_e | float 855 150 Parenting Confidence Score at follow-up experience_end|byte 990 15 Parent's experience of care satisfaction_end|byte 990 15 Satisfaction with care gc_end | byte 990 15 Global change at day 14 ----------------------------------------------------------------------------- Patterns of missing values +----------------------------+ | _pattern _mv _freq | |----------------------------| | ++++++++++++ 0 697 | | ++++++.+++++ 1 98 | | ++++++.+.+++ 2 71 | | ++++++++.+++ 1 64 | | ++.+++++++++ 1 31 | |----------------------------| | ..+......... 11 15 | | ++.+++.+++++ 2 14 | | +++++++.++++ 1 10 | | ++++++..++++ 2 5 | +----------------------------+ . . ttest nmis, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 510 1.158824 .0365156 .8246393 1.087084 1.230563 Test gro | 480 .9104167 .0401777 .8802494 .8314703 .989363 ---------+-------------------------------------------------------------------- combined | 990 1.038384 .0273527 .8606329 .9847078 1.09206 ---------+-------------------------------------------------------------------- diff | .2484069 .054185 .142076 .3547377 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = 4.5844 Ho: diff = 0 degrees of freedom = 988 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 0.0000 . . * 3.1.2 Secondary outcomes dataset (wide) . use source_files/dataset_wide.dta, clear . . mdesc // describing missing data Variable | Missing Total Percent Missing ----------------+----------------------------------------------- id | 0 67 0.00 pract | 44 67 65.67 group | 1 67 1.49 allocation~s | 1 67 1.49 perprotocol | 1 67 1.49 date_b | 1 67 1.49 date_end | 8 67 11.94 days | 8 67 11.94 visits | 3 67 4.48 parent | 1 67 1.49 gender | 1 67 1.49 age | 1 67 1.49 age_cat | 1 67 1.49 bw | 1 67 1.49 weight_b | 5 67 7.46 weight_end | 10 67 14.93 weight_cha~e | 14 67 20.90 ct_b | 3 67 4.48 sibling | 1 67 1.49 parent_age | 1 67 1.49 parent_type | 1 67 1.49 expectation | 1 67 1.49 d0 | 1 67 1.49 d1 | 18 67 26.87 d2 | 13 67 19.40 d3 | 14 67 20.90 d4 | 14 67 20.90 d5 | 13 67 19.40 d6 | 14 67 20.90 d7 | 13 67 19.40 d8 | 13 67 19.40 d9 | 13 67 19.40 d10 | 15 67 22.39 d11 | 13 67 19.40 d12 | 16 67 23.88 d13 | 15 67 22.39 d14 | 18 67 26.87 pcs_b | 2 67 2.99 pcs_e | 10 67 14.93 pcs_cat | 1 67 1.49 help_belie~d | 1 67 1.49 experience~d | 1 67 1.49 experience~g | 1 67 1.49 satisfacti~d | 1 67 1.49 gc_end | 1 67 1.49 ur_reported | 1 67 1.49 ur_type1 | 1 67 1.49 ur_type2 | 1 67 1.49 ur_type3 | 1 67 1.49 ur_type4 | 1 67 1.49 ur_type5 | 1 67 1.49 ur_type6 | 1 67 1.49 ur_type7_txt | 40 67 59.70 ad_sleep | 0 67 0.00 ad_feeding | 0 67 0.00 ad_diet | 0 67 0.00 ad_handling | 0 67 0.00 ad_env | 0 67 0.00 ad_beh | 0 67 0.00 ad_reas | 0 67 0.00 ad_other | 0 67 0.00 other_care | 1 67 1.49 other_care1 | 1 67 1.49 other_care2 | 1 67 1.49 other_care3 | 1 67 1.49 other_care4 | 1 67 1.49 other_care5 | 1 67 1.49 other_care6 | 1 67 1.49 other_care7 | 1 67 1.49 other_care8 | 1 67 1.49 other_care~t | 48 67 71.64 pharm1 | 1 67 1.49 pharm2 | 1 67 1.49 pharm3 | 1 67 1.49 pharm4 | 1 67 1.49 pharm5 | 1 67 1.49 pharm6 | 1 67 1.49 pharm7 | 1 67 1.49 pharm7_txt | 45 67 67.16 ur_type7 | 1 67 1.49 inst | 1 67 1.49 _merge | 0 67 0.00 origin_b | 0 67 0.00 baseline | 1 67 1.49 ----------------+----------------------------------------------- . . egen nmis=rowmiss(id day pract group allocation_guess perprotocol /// > visits age expectation baseline d0 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 /// > d12 d13 d14 pcs_b pcs_e experience_end /// > satisfaction_end gc_end ur_reported ad_sleep ad_feeding ad_diet ad_handling /// > ad_env ad_beh other_care1 other_care2 other_care3 other_care4 other_care5 /// > other_care6 other_care7 pharm1 pharm2 pharm3 pharm4 pharm5 pharm6) . . mvpatterns id day group allocation_guess /// > visits age expectation baseline d0 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 /// > d12 d13 d14 pcs_b pcs_e experience_end /// > satisfaction_end gc_end variables with no mv's: id Variable | type obs mv variable label -------------+--------------------------------------------------------------------- days | float 59 8 Duration of follow-up (days) group | byte 66 1 Group allocation allocation_guess|byte 66 1 Group allocation guess visits | long 64 3 Number of sessions age | byte 66 1 Infant age (days) expectation | byte 66 1 Expectations baseline | float 66 1 Crying time day before treatment (minutes) d0 | float 66 1 Crying time day before treatment (minutes) d1 | float 49 18 Crying time on treatment day (minutes) d2 | float 54 13 Crying time one day after treatment (minutes) d3 | float 53 14 Crying time two days after treatment (minutes) d4 | float 53 14 Crying time three days after treatment (minutes) d5 | float 54 13 Crying time four days after treatment (minutes) d6 | float 53 14 Crying time five days after treatment (minutes) d7 | float 54 13 Crying time six days after treatment (minutes) d8 | float 54 13 Crying time seven days after treatment (minutes) d9 | float 54 13 Crying time eight days after treatment (minutes) d10 | float 52 15 Crying time nine days after treatment (minutes) d11 | float 54 13 Crying time 11 days after treatment (minutes) d12 | float 51 16 Crying time 12 days after treatment (minutes) d13 | float 52 15 Crying time 13 days after treatment (minutes) d14 | float 49 18 Crying time 14 days after treatment (minutes) pcs_b | float 65 2 Parenting Confidence Score at baseline pcs_e | float 57 10 Parenting Confidence Score at follow-up experience_end|byte 66 1 Parent's experience of care satisfaction_end|byte 66 1 Satisfaction with care gc_end | byte 66 1 Global change at day 14 ----------------------------------------------------------------------------------- Patterns of missing values +-------------------------------------------+ | _pattern _mv _freq | |-------------------------------------------| | +++++++++++++++++++++++++++ 0 34 | | ++++++++.++++++++++++++++++ 1 5 | | ++++++++..............+++++ 14 5 | | .+++++++..............+.+++ 16 4 | | +++++++++++++++++++++.+++++ 1 3 | |-------------------------------------------| | +++.+++++++++++++++++++++++ 1 2 | | .++++++++++++++++++++++.+++ 2 2 | | +++++++++++++++++++++++.+++ 1 1 | | ++++++++++.++++++++++++++++ 1 1 | | +++++++++++++++++++...+++++ 3 1 | |-------------------------------------------| | +++++++++++++.++..+++++++++ 3 1 | | +++++++++...+++++++++++++++ 3 1 | | .+++++++.++++++++++++++.+++ 3 1 | | ++++++++++++..+++++..++++++ 4 1 | | +++++++++++++++++......++++ 6 1 | |-------------------------------------------| | +++++++++++.++..+.+.+.+++++ 6 1 | | ++++++++..............+.+++ 15 1 | | +++.++++..............+++++ 15 1 | | ...+....................... 26 1 | +-------------------------------------------+ . . * 3.2 Participant description . use source_files/dataset_wide.dta, clear . ttest d0=d1 if(group==0) Paired t test ------------------------------------------------------------------------------ Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- d0 | 28 223.1786 18.15505 96.06751 185.9275 260.4297 d1 | 28 151.6071 15.58 82.4416 119.6396 183.5747 ---------+-------------------------------------------------------------------- diff | 28 71.57143 16.18776 85.65756 38.3569 104.786 ------------------------------------------------------------------------------ mean(diff) = mean(d0 - d1) t = 4.4213 Ho: mean(diff) = 0 degrees of freedom = 27 Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0 Pr(T < t) = 0.9999 Pr(|T| > |t|) = 0.0001 Pr(T > t) = 0.0001 . ttest d0=d1 if(group==1) Paired t test ------------------------------------------------------------------------------ Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- d0 | 21 222.619 20.23699 92.73752 180.4054 264.8327 d1 | 21 160.8571 19.72577 90.39485 119.7099 202.0044 ---------+-------------------------------------------------------------------- diff | 21 61.7619 18.29297 83.82894 23.60343 99.92038 ------------------------------------------------------------------------------ mean(diff) = mean(d0 - d1) t = 3.3763 Ho: mean(diff) = 0 degrees of freedom = 20 Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0 Pr(T < t) = 0.9985 Pr(|T| > |t|) = 0.0030 Pr(T > t) = 0.0015 . ttest d1=d2 if(group==0) Paired t test ------------------------------------------------------------------------------ Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- d1 | 28 151.6071 15.58 82.4416 119.6396 183.5747 d2 | 28 145.4286 17.78974 94.13443 108.927 181.9301 ---------+-------------------------------------------------------------------- diff | 28 6.178571 12.40072 65.61846 -19.26561 31.62275 ------------------------------------------------------------------------------ mean(diff) = mean(d1 - d2) t = 0.4982 Ho: mean(diff) = 0 degrees of freedom = 27 Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0 Pr(T < t) = 0.6888 Pr(|T| > |t|) = 0.6223 Pr(T > t) = 0.3112 . ttest d1=d2 if(group==1) Paired t test ------------------------------------------------------------------------------ Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- d1 | 20 164.4 20.40077 91.23503 121.7007 207.0993 d2 | 20 152.6 14.93854 66.80719 121.3333 183.8667 ---------+-------------------------------------------------------------------- diff | 20 11.8 17.23576 77.08068 -24.27487 47.87487 ------------------------------------------------------------------------------ mean(diff) = mean(d1 - d2) t = 0.6846 Ho: mean(diff) = 0 degrees of freedom = 19 Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0 Pr(T < t) = 0.7491 Pr(|T| > |t|) = 0.5018 Pr(T > t) = 0.2509 . . * 3.2.1 Gender . tab gender group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 1 stage 1: enumerations = 0 | Group allocation Gender | Control g Test grou | Total -----------+----------------------+---------- Female | 19 17 | 36 | 55.88 53.12 | 54.55 -----------+----------------------+---------- Male | 15 14 | 29 | 44.12 43.75 | 43.94 -----------+----------------------+---------- Missing | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.900 . . * 3.2.2 Age . tab age_cat group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 6: enumerations = 1 stage 5: enumerations = 2 stage 4: enumerations = 3 stage 3: enumerations = 7 stage 2: enumerations = 14 stage 1: enumerations = 0 | Group allocation Infant age | Control g Test grou | Total --------------+----------------------+---------- 1–2 weeks | 1 0 | 1 | 2.94 0.00 | 1.52 --------------+----------------------+---------- 3–4 weeks | 5 5 | 10 | 14.71 15.62 | 15.15 --------------+----------------------+---------- 5–6 weeks | 11 15 | 26 | 32.35 46.88 | 39.39 --------------+----------------------+---------- 7–8 weeks | 12 8 | 20 | 35.29 25.00 | 30.30 --------------+----------------------+---------- 9–10 weeks | 4 4 | 8 | 11.76 12.50 | 12.12 --------------+----------------------+---------- 11–12 weeks | 1 0 | 1 | 2.94 0.00 | 1.52 --------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.720 . ttest age, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 34 42.52941 2.631983 15.34697 37.1746 47.88422 Test gro | 32 39.78125 2.357527 13.33619 34.97304 44.58946 ---------+-------------------------------------------------------------------- combined | 66 41.19697 1.768043 14.36365 37.66594 44.728 ---------+-------------------------------------------------------------------- diff | 2.748162 3.548654 -4.34109 9.837413 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = 0.7744 Ho: diff = 0 degrees of freedom = 64 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.7792 Pr(|T| > |t|) = 0.4415 Pr(T > t) = 0.2208 . . * 3.2.3 Weights . ttest bw, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 34 3.354118 .0651973 .3801624 3.221473 3.486763 Test gro | 32 3.398594 .0943719 .5338482 3.206121 3.591067 ---------+-------------------------------------------------------------------- combined | 66 3.375682 .0563801 .4580342 3.263083 3.488281 ---------+-------------------------------------------------------------------- diff | -.0444761 .1135539 -.2713262 .182374 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -0.3917 Ho: diff = 0 degrees of freedom = 64 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.3483 Pr(|T| > |t|) = 0.6966 Pr(T > t) = 0.6517 . ttest weight_b, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 33 4.47197 .1309133 .7520399 4.205308 4.738631 Test gro | 29 4.267586 .1281174 .6899335 4.00515 4.530023 ---------+-------------------------------------------------------------------- combined | 62 4.376371 .0920865 .7250897 4.192233 4.560509 ---------+-------------------------------------------------------------------- diff | .2043835 .1842089 -.1640893 .5728562 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = 1.1095 Ho: diff = 0 degrees of freedom = 60 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.8642 Pr(|T| > |t|) = 0.2716 Pr(T > t) = 0.1358 . . * 3.2.5 Mother's age . tab parent_age group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 7: enumerations = 1 stage 6: enumerations = 2 stage 5: enumerations = 3 stage 4: enumerations = 2 stage 3: enumerations = 2 stage 2: enumerations = 1 stage 1: enumerations = 0 Parent age | Group allocation (years) | Control g Test grou | Total -----------+----------------------+---------- 21-25 | 2 1 | 3 | 5.88 3.12 | 4.55 -----------+----------------------+---------- 26-30 | 9 7 | 16 | 26.47 21.88 | 24.24 -----------+----------------------+---------- 31-35 | 15 14 | 29 | 44.12 43.75 | 43.94 -----------+----------------------+---------- 36-40 | 8 7 | 15 | 23.53 21.88 | 22.73 -----------+----------------------+---------- 41-45 | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- 46-50 | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Missing | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.959 . . * 3.2.6 Siblings . tab sibling group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 5: enumerations = 1 stage 4: enumerations = 2 stage 3: enumerations = 5 stage 2: enumerations = 9 stage 1: enumerations = 0 Number of | Group allocation siblings | Control g Test grou | Total -------------+----------------------+---------- None | 21 14 | 35 | 61.76 43.75 | 53.03 -------------+----------------------+---------- One | 9 14 | 23 | 26.47 43.75 | 34.85 -------------+----------------------+---------- Two | 2 2 | 4 | 5.88 6.25 | 6.06 -------------+----------------------+---------- Three | 2 1 | 3 | 5.88 3.12 | 4.55 -------------+----------------------+---------- Missing | 0 1 | 1 | 0.00 3.12 | 1.52 -------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.445 . . * 3.2.7 Co-parenting . tab parent_type group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation Parenting type | Control g Test grou | Total ----------------+----------------------+---------- Co-parenting | 34 31 | 65 | 100.00 96.88 | 98.48 ----------------+----------------------+---------- Missing | 0 1 | 1 | 0.00 3.12 | 1.52 ----------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.485 1-sided Fisher's exact = 0.485 . . . * 3.2.9 Expected response from osteopathic care . tab expectation group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 6: enumerations = 1 stage 5: enumerations = 3 stage 4: enumerations = 4 stage 3: enumerations = 7 stage 2: enumerations = 10 stage 1: enumerations = 0 | Group allocation Expectations | Control g Test grou | Total --------------+----------------------+---------- Very well | 1 2 | 3 | 2.94 6.25 | 4.55 --------------+----------------------+---------- Well | 10 7 | 17 | 29.41 21.88 | 25.76 --------------+----------------------+---------- Unsure | 17 17 | 34 | 50.00 53.12 | 51.52 --------------+----------------------+---------- Not very well | 4 2 | 6 | 11.76 6.25 | 9.09 --------------+----------------------+---------- Not well | 1 3 | 4 | 2.94 9.38 | 6.06 --------------+----------------------+---------- Missing | 1 1 | 2 | 2.94 3.12 | 3.03 --------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.827 . . . * 3.3 Baseline values for outcomes . . * 3.3.1 Crying time 24h prior to treatment day . ttest d0, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 34 234.6765 16.12234 94.00861 201.8753 267.4776 Test gro | 32 251.5938 21.10825 119.4063 208.5432 294.6443 ---------+-------------------------------------------------------------------- combined | 66 242.8788 13.11908 106.5799 216.6782 269.0794 ---------+-------------------------------------------------------------------- diff | -16.91728 26.36985 -69.5971 35.76254 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -0.6415 Ho: diff = 0 degrees of freedom = 64 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.2617 Pr(|T| > |t|) = 0.5235 Pr(T > t) = 0.7383 . . * 3.3.2 Parenting condidence score . tab pcs_cat group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 4: enumerations = 1 stage 3: enumerations = 2 stage 2: enumerations = 3 stage 1: enumerations = 0 Parenting Confidence | Group allocation (PCS-score) | Control g Test grou | Total ----------------------+----------------------+---------- Mild clinical range | 6 4 | 10 | 17.65 12.50 | 15.15 ----------------------+----------------------+---------- Moderate clinical ran | 17 15 | 32 | 50.00 46.88 | 48.48 ----------------------+----------------------+---------- Severe clinical range | 11 12 | 23 | 32.35 37.50 | 34.85 ----------------------+----------------------+---------- Missing | 0 1 | 1 | 0.00 3.12 | 1.52 ----------------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.819 . ttest pcs_b, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 34 32.52941 .5652367 3.295868 31.37943 33.67939 Test gro | 31 32.16129 .5867658 3.266974 30.96295 33.35963 ---------+-------------------------------------------------------------------- combined | 65 32.35385 .4045598 3.261665 31.54565 33.16205 ---------+-------------------------------------------------------------------- diff | .3681214 .8150674 -1.260661 1.996904 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = 0.4516 Ho: diff = 0 degrees of freedom = 63 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.6735 Pr(|T| > |t|) = 0.6531 Pr(T > t) = 0.3265 . . . . * ---------------------- 4. HYPOTHESIS TESTING ------------------ . . use Source_files/dataset.dta, clear . . * 4.1 Primary outcome – average reduction in crying time . . * 4.1.1 Mean daily crying time (minutes) from day 1 to day 13 . mixed crying_time if(day>1 & group==1) || id:, var reml Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -1793.1406 Iteration 1: log restricted-likelihood = -1793.1406 Computing standard errors: Mixed-effects REML regression Number of obs = 320 Group variable: id Number of groups = 26 Obs per group: min = 7 avg = 12.3 max = 13 Wald chi2(0) = . Log restricted-likelihood = -1793.1406 Prob > chi2 = . ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | 123.7894 13.48115 9.18 0.000 97.3668 150.2119 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 4432.745 1336.23 2455.174 8003.195 -----------------------------+------------------------------------------------ var(Residual) | 3524.222 290.6636 2998.193 4142.542 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 184.36 Prob >= chibar2 = 0.0000 . mixed crying_time if(day>1 & group==0) || id:, var reml Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -2019.3786 Iteration 1: log restricted-likelihood = -2019.3786 Computing standard errors: Mixed-effects REML regression Number of obs = 367 Group variable: id Number of groups = 29 Obs per group: min = 9 avg = 12.7 max = 13 Wald chi2(0) = . Log restricted-likelihood = -2019.3786 Prob > chi2 = . ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | 115.0564 9.189774 12.52 0.000 97.04477 133.068 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 2211.271 655.3777 1236.979 3952.952 -----------------------------+------------------------------------------------ var(Residual) | 2987.465 229.8213 2569.338 3473.638 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 131.37 Prob >= chibar2 = 0.0000 . . mixed crying_time group baseline expectation2 age_cat day if(day>1) || id:, var reml // adjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3763.1735 Iteration 1: log restricted-likelihood = -3763.1735 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(5) = 114.81 Log restricted-likelihood = -3763.1735 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 2.191086 11.54348 0.19 0.849 -20.43371 24.81589 baseline | .3691646 .0587057 6.29 0.000 .2541037 .4842256 expectation2 | -4.343393 6.100005 -0.71 0.476 -16.29918 7.612398 age_cat | -17.0678 5.892208 -2.90 0.004 -28.61632 -5.519285 day | -4.351613 .5612858 -7.75 0.000 -5.451713 -3.251513 _cons | 117.7239 21.88063 5.38 0.000 74.8387 160.6092 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 1546.397 359.4884 980.4906 2438.925 -----------------------------+------------------------------------------------ var(Residual) | 2961.684 166.8095 2652.143 3307.353 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 159.86 Prob >= chibar2 = 0.0000 . mixed crying_time group if(day>1) || id:, var reml // unadjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3815 Iteration 1: log restricted-likelihood = -3815 Computing standard errors: Mixed-effects REML regression Number of obs = 687 Group variable: id Number of groups = 55 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(1) = 0.30 Log restricted-likelihood = -3815 Prob > chi2 = 0.5832 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 8.794648 16.02573 0.55 0.583 -22.61521 40.20451 _cons | 115.0435 11.00961 10.45 0.000 93.46509 136.622 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3257.349 684.2765 2157.998 4916.744 -----------------------------+------------------------------------------------ var(Residual) | 3237.214 182.1138 2899.252 3614.572 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 326.16 Prob >= chibar2 = 0.0000 . . . * 4.1.2 Mean daily crying time (minutes) from day 1 to day 6 . mixed crying_time if(day>1 & day<8 & group==1) || id:, var reml Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -853.84149 Iteration 1: log restricted-likelihood = -853.84149 Computing standard errors: Mixed-effects REML regression Number of obs = 150 Group variable: id Number of groups = 26 Obs per group: min = 3 avg = 5.8 max = 6 Wald chi2(0) = . Log restricted-likelihood = -853.84149 Prob > chi2 = . ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | 138.8496 13.14501 10.56 0.000 113.0858 164.6133 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3800.104 1270.32 1973.563 7317.115 -----------------------------+------------------------------------------------ var(Residual) | 3921.707 497.8004 3057.935 5029.469 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 52.09 Prob >= chibar2 = 0.0000 . mixed crying_time if(day>1 & day<8 & group==0) || id:, var reml Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -950.34606 Iteration 1: log restricted-likelihood = -950.34606 Computing standard errors: Mixed-effects REML regression Number of obs = 171 Group variable: id Number of groups = 29 Obs per group: min = 4 avg = 5.9 max = 6 Wald chi2(0) = . Log restricted-likelihood = -950.34606 Prob > chi2 = . ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | 128.7852 11.1376 11.56 0.000 106.9559 150.6145 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3095.354 965.9009 1679.168 5705.932 -----------------------------+------------------------------------------------ var(Residual) | 2944.396 349.6141 2333.058 3715.924 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 63.32 Prob >= chibar2 = 0.0000 . . mixed crying_time group baseline expectation2 age_cat day if(day>1 & day<8) || id:, var reml // adjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -1775.9696 Iteration 1: log restricted-likelihood = -1775.9696 Computing standard errors: Mixed-effects REML regression Number of obs = 321 Group variable: id Number of groups = 55 Obs per group: min = 3 avg = 5.8 max = 6 Wald chi2(5) = 65.22 Log restricted-likelihood = -1775.9696 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 2.969227 12.36019 0.24 0.810 -21.2563 27.19476 baseline | .3872455 .0630111 6.15 0.000 .2637461 .510745 expectation2 | -3.574106 6.542162 -0.55 0.585 -16.39651 9.248296 age_cat | -19.1235 6.316318 -3.03 0.002 -31.50326 -6.743746 day | -6.05772 1.874182 -3.23 0.001 -9.73105 -2.384391 _cons | 123.4402 24.49608 5.04 0.000 75.42881 171.4517 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 1480.498 417.328 852.0561 2572.453 -----------------------------+------------------------------------------------ var(Residual) | 3281.277 285.6355 2766.596 3891.707 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 43.38 Prob >= chibar2 = 0.0000 . mixed crying_time group if(day>1 & day<8) || id:, var reml // unadjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -1805.7072 Iteration 1: log restricted-likelihood = -1805.7072 Computing standard errors: Mixed-effects REML regression Number of obs = 321 Group variable: id Number of groups = 55 Obs per group: min = 3 avg = 5.8 max = 6 Wald chi2(1) = 0.34 Log restricted-likelihood = -1805.7072 Prob > chi2 = 0.5574 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 10.04492 17.11955 0.59 0.557 -23.50877 43.59861 _cons | 128.7942 11.75448 10.96 0.000 105.7558 151.8325 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3427.184 782.1376 2191.189 5360.374 -----------------------------+------------------------------------------------ var(Residual) | 3400.296 294.8563 2868.83 4030.218 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 114.95 Prob >= chibar2 = 0.0000 . . * 4.1.3 Mean daily crying time (minutes) from day 7 to day 13 . mixed crying_time if(day>7 & group==1) || id:, var reml Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -934.8028 Iteration 1: log restricted-likelihood = -934.8028 Computing standard errors: Mixed-effects REML regression Number of obs = 170 Group variable: id Number of groups = 26 Obs per group: min = 2 avg = 6.5 max = 7 Wald chi2(0) = . Log restricted-likelihood = -934.8028 Prob > chi2 = . ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | 110.9816 15.01132 7.39 0.000 81.5599 140.4032 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 5461.169 1656.855 3013.29 9897.608 -----------------------------+------------------------------------------------ var(Residual) | 2417.18 284.8384 1918.687 3045.186 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 127.54 Prob >= chibar2 = 0.0000 . mixed crying_time if(day>7 & group==0) || id:, var reml Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -1048.5241 Iteration 1: log restricted-likelihood = -1048.5241 Computing standard errors: Mixed-effects REML regression Number of obs = 196 Group variable: id Number of groups = 29 Obs per group: min = 4 avg = 6.8 max = 7 Wald chi2(0) = . Log restricted-likelihood = -1048.5241 Prob > chi2 = . ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | 103.53 9.357759 11.06 0.000 85.1891 121.8708 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 2245.998 682.0423 1238.583 4072.807 -----------------------------+------------------------------------------------ var(Residual) | 1955.344 214.1147 1577.664 2423.437 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 83.62 Prob >= chibar2 = 0.0000 . . mixed crying_time group baseline expectation2 age_cat day if(day>7) || id:, var reml // adjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -1962.1239 Iteration 1: log restricted-likelihood = -1962.1239 Computing standard errors: Mixed-effects REML regression Number of obs = 366 Group variable: id Number of groups = 55 Obs per group: min = 2 avg = 6.7 max = 7 Wald chi2(5) = 45.44 Log restricted-likelihood = -1962.1239 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 1.295137 13.91606 0.09 0.926 -25.97983 28.57011 baseline | .3540548 .0706397 5.01 0.000 .2156035 .4925061 expectation2 | -4.836158 7.34784 -0.66 0.510 -19.23766 9.565344 age_cat | -15.46833 7.08413 -2.18 0.029 -29.35297 -1.583686 day | -4.148703 1.211252 -3.43 0.001 -6.522714 -1.774692 _cons | 117.739 29.03986 4.05 0.000 60.82197 174.6561 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 2264.577 520.9925 1442.641 3554.807 -----------------------------+------------------------------------------------ var(Residual) | 2099.06 168.6912 1793.155 2457.151 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 140.77 Prob >= chibar2 = 0.0000 . mixed crying_time group if(day>7) || id:, var reml // unadjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -1986.4325 Iteration 1: log restricted-likelihood = -1986.4325 Computing standard errors: Mixed-effects REML regression Number of obs = 366 Group variable: id Number of groups = 55 Obs per group: min = 2 avg = 6.7 max = 7 Wald chi2(1) = 0.19 Log restricted-likelihood = -1986.4325 Prob > chi2 = 0.6633 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 7.526532 17.29071 0.44 0.663 -26.36263 41.41569 _cons | 103.5847 11.86536 8.73 0.000 80.32901 126.8404 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3756.977 797.6266 2478.128 5695.78 -----------------------------+------------------------------------------------ var(Residual) | 2169.284 174.0076 1853.694 2538.604 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 222.96 Prob >= chibar2 = 0.0000 . . * 4.1.4 Daily crying time (minutes) . . // Day 1 after treatment . ttest crying_time if(day==2), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 29 148.1724 17.38325 93.61169 112.5644 183.7804 Test gro | 25 147.44 12.61957 63.09786 121.3945 173.4855 ---------+-------------------------------------------------------------------- combined | 54 147.8333 10.91431 80.20346 125.942 169.7247 ---------+-------------------------------------------------------------------- diff | .7324138 22.09798 -43.61043 45.07526 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = 0.0331 Ho: diff = 0 degrees of freedom = 52 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.5132 Pr(|T| > |t|) = 0.9737 Pr(T > t) = 0.4868 . mixed crying_time group baseline expectation2 age_cat day if(day==2) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 54 Wald chi2(4) = 18.88 Log likelihood = -304.78872 Prob > chi2 = 0.0008 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -10.00842 18.84817 -0.53 0.595 -46.95015 26.93331 baseline | .3577666 .0976416 3.66 0.000 .1663926 .5491406 expectation2 | 2.239983 9.955606 0.22 0.822 -17.27265 21.75261 age_cat | -11.38672 9.5755 -1.19 0.234 -30.15435 7.380915 _cons | 90.13624 34.79409 2.59 0.010 21.94108 158.3314 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 4677.85 900.2527 3207.986 6821.189 ------------------------------------------------------------------------------ . mixed crying_time group if(day==2) Mixed-effects ML regression Number of obs = 54 Wald chi2(1) = 0.00 Log likelihood = -312.88402 Prob > chi2 = 0.9731 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -.7324138 21.6849 -0.03 0.973 -43.23404 41.76921 _cons | 148.1724 14.75471 10.04 0.000 119.2537 177.0911 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 6313.339 1215.003 4329.575 9206.041 ------------------------------------------------------------------------------ . . // Day 2 after treatment . ttest crying_time if(day==3), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 29 140.6897 14.63094 78.79 110.7195 170.6598 Test gro | 24 136.5833 18.10246 88.68357 99.13555 174.0311 ---------+-------------------------------------------------------------------- combined | 53 138.8302 11.34835 82.61722 116.0581 161.6023 ---------+-------------------------------------------------------------------- diff | 4.106322 23.01362 -42.09545 50.3081 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = 0.1784 Ho: diff = 0 degrees of freedom = 51 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.5705 Pr(|T| > |t|) = 0.8591 Pr(T > t) = 0.4295 . mixed crying_time group baseline expectation2 age_cat day if(day==3) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 53 Wald chi2(4) = 21.58 Log likelihood = -299.60206 Prob > chi2 = 0.0002 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -9.96483 19.19383 -0.52 0.604 -47.58405 27.65439 baseline | .3743882 .0986711 3.79 0.000 .1809963 .56778 expectation2 | -11.86243 10.10464 -1.17 0.240 -31.66715 7.942287 age_cat | -21.52812 9.875557 -2.18 0.029 -40.88386 -2.172386 _cons | 135.4915 35.29683 3.84 0.000 66.31095 204.672 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 4759.324 924.5328 3252.32 6964.617 ------------------------------------------------------------------------------ . mixed crying_time group if(day==3) Mixed-effects ML regression Number of obs = 53 Wald chi2(1) = 0.03 Log likelihood = -308.63599 Prob > chi2 = 0.8557 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -4.106322 22.57523 -0.18 0.856 -48.35295 40.14031 _cons | 140.6897 15.19147 9.26 0.000 110.9149 170.4644 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 6692.642 1300.094 4573.468 9793.764 ------------------------------------------------------------------------------ . . // Day 3 after treatment . ttest crying_time if(day==4), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 29 134.3448 12.17689 65.57454 109.4016 159.288 Test gro | 24 164.5 24.49919 120.021 113.8196 215.1804 ---------+-------------------------------------------------------------------- combined | 53 148 12.97058 94.42722 121.9726 174.0274 ---------+-------------------------------------------------------------------- diff | -30.15517 25.97055 -82.29324 21.98289 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -1.1611 Ho: diff = 0 degrees of freedom = 51 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1255 Pr(|T| > |t|) = 0.2510 Pr(T > t) = 0.8745 . mixed crying_time group baseline expectation2 age_cat day if(day==4) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 53 Wald chi2(4) = 68.68 Log likelihood = -293.71034 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 18.96182 17.34071 1.09 0.274 -15.02535 52.949 baseline | .616734 .0881478 7.00 0.000 .4439674 .7895005 expectation2 | -17.54907 9.468733 -1.85 0.064 -36.10744 1.00931 age_cat | -27.85591 8.642371 -3.22 0.001 -44.79465 -10.91717 _cons | 102.8849 32.0382 3.21 0.001 40.09122 165.6787 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 3810.562 740.2289 2603.976 5576.234 ------------------------------------------------------------------------------ . mixed crying_time group if(day==4) Mixed-effects ML regression Number of obs = 53 Wald chi2(1) = 1.40 Log likelihood = -315.04248 Prob > chi2 = 0.2365 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 30.15517 25.47583 1.18 0.237 -19.77654 80.08689 _cons | 134.3448 17.14336 7.84 0.000 100.7444 167.9452 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 8522.954 1655.645 5824.225 12472.17 ------------------------------------------------------------------------------ . . // Day 4 after treatment . ttest crying_time if(day==5), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 28 117.4286 13.92149 73.66563 88.86402 145.9931 Test gro | 26 144.9231 17.23127 87.86259 109.4346 180.4115 ---------+-------------------------------------------------------------------- combined | 54 130.6667 11.05393 81.22947 108.4953 152.838 ---------+-------------------------------------------------------------------- diff | -27.49451 22.0069 -71.65458 16.66556 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -1.2494 Ho: diff = 0 degrees of freedom = 52 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1086 Pr(|T| > |t|) = 0.2171 Pr(T > t) = 0.8914 . mixed crying_time group baseline expectation2 age_cat day if(day==5) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 54 Wald chi2(4) = 22.33 Log likelihood = -304.22736 Prob > chi2 = 0.0002 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 16.33116 18.69588 0.87 0.382 -20.31209 52.97442 baseline | .249655 .094781 2.63 0.008 .0638877 .4354223 expectation2 | 16.79324 9.838571 1.71 0.088 -2.490004 36.07649 age_cat | -20.71658 9.656266 -2.15 0.032 -39.64251 -1.790644 _cons | 74.16029 35.23332 2.10 0.035 5.104255 143.2163 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 4581.597 881.7288 3141.978 6680.835 ------------------------------------------------------------------------------ . mixed crying_time group if(day==5) Mixed-effects ML regression Number of obs = 54 Wald chi2(1) = 1.62 Log likelihood = -312.77247 Prob > chi2 = 0.2030 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 27.49451 21.59552 1.27 0.203 -14.83193 69.82094 _cons | 117.4286 14.98488 7.84 0.000 88.05874 146.7984 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 6287.309 1209.993 4311.725 9168.085 ------------------------------------------------------------------------------ . . // Day 5 after treatment . ttest crying_time if(day==6), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 27 127.5556 14.57736 75.7462 97.59136 157.5198 Test gro | 26 116.0769 13.29209 67.77665 88.70134 143.4525 ---------+-------------------------------------------------------------------- combined | 53 121.9245 9.819889 71.48987 102.2195 141.6296 ---------+-------------------------------------------------------------------- diff | 11.47863 19.76969 -28.21067 51.16794 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = 0.5806 Ho: diff = 0 degrees of freedom = 51 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.7180 Pr(|T| > |t|) = 0.5641 Pr(T > t) = 0.2820 . mixed crying_time group baseline expectation2 age_cat day if(day==6) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 53 Wald chi2(4) = 22.34 Log likelihood = -291.6641 Prob > chi2 = 0.0002 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -18.25037 16.59392 -1.10 0.271 -50.77385 14.27311 baseline | .3416236 .0838418 4.07 0.000 .1772967 .5059506 expectation2 | -2.781631 8.755437 -0.32 0.751 -19.94197 14.37871 age_cat | -12.16741 8.689095 -1.40 0.161 -29.19772 4.862905 _cons | 87.27519 31.47042 2.77 0.006 25.5943 148.9561 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 3527.397 685.2221 2410.473 5161.861 ------------------------------------------------------------------------------ . mixed crying_time group if(day==6) Mixed-effects ML regression Number of obs = 53 Wald chi2(1) = 0.35 Log likelihood = -300.81083 Prob > chi2 = 0.5539 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -11.47863 19.39309 -0.59 0.554 -49.48839 26.53112 _cons | 127.5556 13.583 9.39 0.000 100.9334 154.1777 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 4981.444 967.6812 3404.107 7289.659 ------------------------------------------------------------------------------ . . // Day 6 after treatment . ttest crying_time if(day==7), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 29 110.8966 13.19215 71.04191 83.87365 137.9194 Test gro | 25 132.24 17.66808 88.34038 95.77488 168.7051 ---------+-------------------------------------------------------------------- combined | 54 120.7778 10.81461 79.47081 99.08642 142.4691 ---------+-------------------------------------------------------------------- diff | -21.34345 21.69539 -64.87843 22.19153 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -0.9838 Ho: diff = 0 degrees of freedom = 52 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1649 Pr(|T| > |t|) = 0.3298 Pr(T > t) = 0.8351 . mixed crying_time group baseline expectation2 age_cat day if(day==7) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 54 Wald chi2(4) = 31.75 Log likelihood = -299.90386 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 16.46077 17.37152 0.95 0.343 -17.58679 50.50833 baseline | .4298114 .0868832 4.95 0.000 .2595236 .6000993 expectation2 | -13.1499 9.48581 -1.39 0.166 -31.74175 5.441943 age_cat | -16.40999 8.717891 -1.88 0.060 -33.49675 .6767577 _cons | 83.81561 32.27104 2.60 0.009 20.56553 147.0657 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 3903.673 751.2623 2677.07 5692.294 ------------------------------------------------------------------------------ . mixed crying_time group if(day==7) Mixed-effects ML regression Number of obs = 54 Wald chi2(1) = 1.01 Log likelihood = -311.89114 Prob > chi2 = 0.3161 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 21.34345 21.28983 1.00 0.316 -20.38385 63.07075 _cons | 110.8966 14.4859 7.66 0.000 82.50472 139.2884 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 6085.394 1171.135 4173.254 8873.654 ------------------------------------------------------------------------------ . . // Day 7 after treatment . ttest crying_time if(day==8), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 29 115.7931 10.62324 57.20788 94.03239 137.5538 Test gro | 25 139.84 18.28977 91.44884 102.0918 177.5882 ---------+-------------------------------------------------------------------- combined | 54 126.9259 10.24012 75.24923 106.3868 147.465 ---------+-------------------------------------------------------------------- diff | -24.0469 20.46326 -65.10944 17.01564 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -1.1751 Ho: diff = 0 degrees of freedom = 52 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1226 Pr(|T| > |t|) = 0.2453 Pr(T > t) = 0.8774 . mixed crying_time group baseline expectation2 age_cat day if(day==8) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 54 Wald chi2(4) = 33.18 Log likelihood = -296.51018 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 16.72628 16.31339 1.03 0.305 -15.24738 48.69994 baseline | .3322638 .0815909 4.07 0.000 .1723485 .4921791 expectation2 | -2.501629 8.908012 -0.28 0.779 -19.96101 14.95775 age_cat | -26.72816 8.186868 -3.26 0.001 -42.77413 -10.68219 _cons | 111.3121 30.30535 3.67 0.000 51.9147 170.7095 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 3442.597 662.5281 2360.872 5019.957 ------------------------------------------------------------------------------ . mixed crying_time group if(day==8) Mixed-effects ML regression Number of obs = 54 Wald chi2(1) = 1.43 Log likelihood = -308.73384 Prob > chi2 = 0.2311 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 24.0469 20.08074 1.20 0.231 -15.31063 63.40442 _cons | 115.7931 13.66321 8.47 0.000 89.0137 142.5725 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 5413.817 1041.89 3712.699 7894.368 ------------------------------------------------------------------------------ . . // Day 8 after treatment . ttest crying_time if(day==9), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 28 105.4286 10.44492 55.26933 83.99736 126.8598 Test gro | 26 111.1154 17.90877 91.31717 74.23158 147.9992 ---------+-------------------------------------------------------------------- combined | 54 108.1667 10.09015 74.14716 87.92839 128.4049 ---------+-------------------------------------------------------------------- diff | -5.686813 20.37215 -46.56652 35.19289 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -0.2791 Ho: diff = 0 degrees of freedom = 52 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.3906 Pr(|T| > |t|) = 0.7812 Pr(T > t) = 0.6094 . mixed crying_time group baseline expectation2 age_cat day if(day==9) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 54 Wald chi2(4) = 19.11 Log likelihood = -300.46554 Prob > chi2 = 0.0007 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 1.586616 17.42275 0.09 0.927 -32.56135 35.73458 baseline | .3468467 .0884906 3.92 0.000 .1734083 .5202851 expectation2 | -9.637881 9.209382 -1.05 0.295 -27.68794 8.412176 age_cat | -12.53183 9.045228 -1.39 0.166 -30.26015 5.196488 _cons | 79.4722 32.5764 2.44 0.015 15.62364 143.3208 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 3985.731 767.0544 2733.344 5811.95 ------------------------------------------------------------------------------ . mixed crying_time group if(day==9) Mixed-effects ML regression Number of obs = 54 Wald chi2(1) = 0.08 Log likelihood = -308.60436 Prob > chi2 = 0.7761 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 5.686813 19.99133 0.28 0.776 -33.49547 44.8691 _cons | 105.4286 13.87176 7.60 0.000 78.24043 132.6167 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 5387.917 1036.905 3694.937 7856.601 ------------------------------------------------------------------------------ . . // Day 9 after treatment . ttest crying_time if(day==10), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 28 115.3214 13.79537 72.99825 87.01566 143.6272 Test gro | 24 109.8333 16.70456 81.83529 75.27732 144.3893 ---------+-------------------------------------------------------------------- combined | 52 112.7885 10.60565 76.47846 91.49672 134.0802 ---------+-------------------------------------------------------------------- diff | 5.488095 21.47201 -37.63971 48.6159 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = 0.2556 Ho: diff = 0 degrees of freedom = 50 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.6003 Pr(|T| > |t|) = 0.7993 Pr(T > t) = 0.3997 . mixed crying_time group baseline expectation2 age_cat day if(day==10) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 52 Wald chi2(4) = 19.59 Log likelihood = -290.49214 Prob > chi2 = 0.0006 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -12.90791 18.4109 -0.70 0.483 -48.99262 23.1768 baseline | .342584 .091536 3.74 0.000 .1631768 .5219912 expectation2 | -5.484322 9.950004 -0.55 0.582 -24.98597 14.01733 age_cat | -16.19776 9.263743 -1.75 0.080 -34.35436 1.958845 _cons | 90.01849 34.24651 2.63 0.009 22.89656 157.1404 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 4166.774 817.1716 2837.041 6119.759 ------------------------------------------------------------------------------ . mixed crying_time group if(day==10) Mixed-effects ML regression Number of obs = 52 Wald chi2(1) = 0.07 Log likelihood = -298.77046 Prob > chi2 = 0.7944 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -5.488095 21.05504 -0.26 0.794 -46.75521 35.77902 _cons | 115.3214 14.30408 8.06 0.000 87.28594 143.3569 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 5728.989 1123.547 3900.71 8414.191 ------------------------------------------------------------------------------ . . // Day 10 after treatment . ttest crying_time if(day==11), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 29 103.931 8.732185 47.02426 86.04396 121.8181 Test gro | 25 108.32 16.0694 80.34702 75.15438 141.4856 ---------+-------------------------------------------------------------------- combined | 54 105.963 8.709737 64.00323 88.49344 123.4325 ---------+-------------------------------------------------------------------- diff | -4.388966 17.62411 -39.75433 30.9764 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -0.2490 Ho: diff = 0 degrees of freedom = 52 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.4022 Pr(|T| > |t|) = 0.8043 Pr(T > t) = 0.5978 . mixed crying_time group baseline expectation2 age_cat day if(day==11) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 54 Wald chi2(4) = 18.75 Log likelihood = -292.65274 Prob > chi2 = 0.0009 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -3.310228 15.08444 -0.22 0.826 -32.87519 26.25474 baseline | .2623839 .0767946 3.42 0.001 .1118693 .4128986 expectation2 | 4.421216 7.855454 0.56 0.574 -10.97519 19.81762 age_cat | -12.04079 7.633496 -1.58 0.115 -27.00217 2.920589 _cons | 63.95083 27.89376 2.29 0.022 9.280065 118.6216 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 2984.279 574.3247 2046.565 4351.642 ------------------------------------------------------------------------------ . mixed crying_time group if(day==11) Mixed-effects ML regression Number of obs = 54 Wald chi2(1) = 0.06 Log likelihood = -300.66822 Prob > chi2 = 0.7997 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 4.388966 17.29466 0.25 0.800 -29.50794 38.28588 _cons | 103.931 11.76753 8.83 0.000 80.86711 126.995 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 4015.765 772.8343 2753.94 5855.744 ------------------------------------------------------------------------------ . . // Day 11 after treatment . ttest crying_time if(day==12), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 27 97.22222 14.50153 75.35216 67.4139 127.0305 Test gro | 24 111.1667 18.47714 90.51911 72.9438 149.3895 ---------+-------------------------------------------------------------------- combined | 51 103.7843 11.5224 82.28641 80.64089 126.9277 ---------+-------------------------------------------------------------------- diff | -13.94444 23.2339 -60.63472 32.74583 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -0.6002 Ho: diff = 0 degrees of freedom = 49 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.2756 Pr(|T| > |t|) = 0.5512 Pr(T > t) = 0.7244 . mixed crying_time group baseline expectation2 age_cat day if(day==12) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 51 Wald chi2(4) = 22.09 Log likelihood = -287.60363 Prob > chi2 = 0.0002 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 4.643728 19.72903 0.24 0.814 -34.02446 43.31192 baseline | .3303868 .0964721 3.42 0.001 .1413051 .5194686 expectation2 | -.5321543 10.52733 -0.05 0.960 -21.16534 20.10103 age_cat | -24.5174 9.766724 -2.51 0.012 -43.65983 -5.374977 _cons | 83.40895 36.71678 2.27 0.023 11.44539 155.3725 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 4631.78 917.2292 3141.846 6828.275 ------------------------------------------------------------------------------ . mixed crying_time group if(day==12) Mixed-effects ML regression Number of obs = 51 Wald chi2(1) = 0.37 Log likelihood = -296.59463 Prob > chi2 = 0.5403 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 13.94444 22.77378 0.61 0.540 -30.69134 58.58023 _cons | 97.22222 15.62268 6.22 0.000 66.60232 127.8421 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 6589.843 1304.983 4470.047 9714.895 ------------------------------------------------------------------------------ . . // Day 12 after treatment . ttest crying_time if(day==13), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 27 93.18519 12.92902 67.18118 66.6092 119.7612 Test gro | 25 113.56 20.00551 100.0275 72.27067 154.8493 ---------+-------------------------------------------------------------------- combined | 52 102.9808 11.69734 84.35069 79.49739 126.4642 ---------+-------------------------------------------------------------------- diff | -20.37481 23.46873 -67.51315 26.76352 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -0.8682 Ho: diff = 0 degrees of freedom = 50 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1947 Pr(|T| > |t|) = 0.3895 Pr(T > t) = 0.8053 . mixed crying_time group baseline expectation2 age_cat day if(day==13) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 52 Wald chi2(4) = 40.60 Log likelihood = -288.89612 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 11.27452 17.71445 0.64 0.524 -23.44517 45.99421 baseline | .5249831 .088735 5.92 0.000 .3510658 .6989005 expectation2 | -14.70831 9.131419 -1.61 0.107 -32.60557 3.18894 age_cat | -11.08339 8.980962 -1.23 0.217 -28.68575 6.518976 _cons | 36.48772 32.93715 1.11 0.268 -28.0679 101.0433 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 3918.687 768.5178 2668.125 5755.393 ------------------------------------------------------------------------------ . mixed crying_time group if(day==13) Mixed-effects ML regression Number of obs = 52 Wald chi2(1) = 0.78 Log likelihood = -303.51004 Prob > chi2 = 0.3760 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 20.37481 23.01298 0.89 0.376 -24.7298 65.47943 _cons | 93.18519 15.95663 5.84 0.000 61.91076 124.4596 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 6874.581 1348.216 4680.712 10096.73 ------------------------------------------------------------------------------ . . // Day 13 after treatment . ttest crying_time if(day==14), by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 28 87.07143 13.32479 70.50817 59.73121 114.4116 Test gro | 21 97.19048 17.63433 80.81065 60.40591 133.975 ---------+-------------------------------------------------------------------- combined | 49 91.40816 10.6359 74.4513 70.02325 112.7931 ---------+-------------------------------------------------------------------- diff | -10.11905 21.66947 -53.71239 33.4743 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -0.4670 Ho: diff = 0 degrees of freedom = 47 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.3213 Pr(|T| > |t|) = 0.6427 Pr(T > t) = 0.6787 . mixed crying_time group baseline expectation2 age_cat day if(day==14) note: day dropped because of collinearity Mixed-effects ML regression Number of obs = 49 Wald chi2(4) = 12.09 Log likelihood = -274.81588 Prob > chi2 = 0.0167 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 10.45814 19.85069 0.53 0.598 -28.4485 49.36477 baseline | .3112125 .094885 3.28 0.001 .1252412 .4971837 expectation2 | -14.32294 10.903 -1.31 0.189 -35.69243 7.046553 age_cat | -6.3978 9.276955 -0.69 0.490 -24.5803 11.7847 _cons | 65.03996 34.99068 1.86 0.063 -3.540509 133.6204 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 4355.077 879.8585 2931.077 6470.898 ------------------------------------------------------------------------------ . mixed crying_time group if(day==14) Mixed-effects ML regression Number of obs = 49 Wald chi2(1) = 0.23 Log likelihood = -280.10653 Prob > chi2 = 0.6335 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 10.11905 21.22263 0.48 0.634 -31.47653 51.71463 _cons | 87.07143 13.89347 6.27 0.000 59.84073 114.3021 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 5404.798 1091.934 3637.565 8030.603 ------------------------------------------------------------------------------ . . . . * 4.2 Secondary outcomes . . use source_files/dataset_wide.dta, clear . gen expectation2=expectation (1 missing value generated) . recode expectation2 1=0 2=1 3=2 6=3 (expectation2: 56 changes made) . label variable expectation2 "Expectations" . label define expectation2_ 0 "Very well" 1 "Well" 3 "Unsure | Missing" 4 "Not very well" 5 "Not well" 2 "Missing" . label value expectation2 expectation2_ . . * 4.2.1 Parenting Confidence Score . ttest pcs_e, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 28 36.21429 .4916153 2.601383 35.20557 37.223 Test gro | 29 35.86207 .5245785 2.824942 34.78752 36.93662 ---------+-------------------------------------------------------------------- combined | 57 36.03509 .3574879 2.698974 35.31895 36.75122 ---------+-------------------------------------------------------------------- diff | .3522167 .7199926 -1.090681 1.795114 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = 0.4892 Ho: diff = 0 degrees of freedom = 55 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.6867 Pr(|T| > |t|) = 0.6266 Pr(T > t) = 0.3133 . ttest pcs_e=pcs_b Paired t test ------------------------------------------------------------------------------ Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- pcs_e | 56 36 .3621724 2.71025 35.27419 36.72581 pcs_b | 56 32.39286 .4577462 3.425459 31.47551 33.3102 ---------+-------------------------------------------------------------------- diff | 56 3.607143 .4724065 3.535167 2.660419 4.553867 ------------------------------------------------------------------------------ mean(diff) = mean(pcs_e - pcs_b) t = 7.6357 Ho: mean(diff) = 0 degrees of freedom = 55 Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0 Pr(T < t) = 1.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 0.0000 . mixed pcs_e group pcs_b expectation2 age_cat Mixed-effects ML regression Number of obs = 56 Wald chi2(4) = 11.69 Log likelihood = -129.48037 Prob > chi2 = 0.0198 ------------------------------------------------------------------------------ pcs_e | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -.323662 .6542376 -0.49 0.621 -1.605944 .9586202 pcs_b | .2484974 .0987436 2.52 0.012 .0549635 .4420312 expectation2 | .4445884 .2771052 1.60 0.109 -.0985277 .9877046 age_cat | -.2183429 .3229004 -0.68 0.499 -.851216 .4145302 _cons | 27.72162 3.225528 8.59 0.000 21.3997 34.04354 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 5.96806 1.127857 4.120702 8.64361 ------------------------------------------------------------------------------ . mixed pcs_e group Mixed-effects ML regression Number of obs = 57 Wald chi2(1) = 0.25 Log likelihood = -136.84501 Prob > chi2 = 0.6185 ------------------------------------------------------------------------------ pcs_e | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | -.3522167 .7072483 -0.50 0.618 -1.738398 1.033965 _cons | 36.21429 .5044679 71.79 0.000 35.22555 37.20302 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | 7.125659 1.334758 4.936059 10.28655 ------------------------------------------------------------------------------ . . * 4.2.2 Perceived changes in symptoms . tab gc_end group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Global change at day | Group allocation 14 | Control g Test grou | Total ---------------------+----------------------+---------- Much improved | 14 16 | 30 | 41.18 50.00 | 45.45 ---------------------+----------------------+---------- Slightly improved | 9 5 | 14 | 26.47 15.62 | 21.21 ---------------------+----------------------+---------- No change | 5 6 | 11 | 14.71 18.75 | 16.67 ---------------------+----------------------+---------- Slightly worse | 0 1 | 1 | 0.00 3.12 | 1.52 ---------------------+----------------------+---------- Much worse | 0 1 | 1 | 0.00 3.12 | 1.52 ---------------------+----------------------+---------- Not reported | 6 3 | 9 | 17.65 9.38 | 13.64 ---------------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(5) = 4.3105 Pr = 0.506 . ranksum gc_end if(gc_end!=8), by(group) Two-sample Wilcoxon rank-sum (Mann-Whitney) test group | obs rank sum expected -------------+--------------------------------- Control grou | 28 804.5 812 Test group ( | 29 848.5 841 -------------+--------------------------------- combined | 57 1653 1653 unadjusted variance 3924.67 adjustment for ties -657.59 ---------- adjusted variance 3267.08 Ho: gc_end(group==Control group (GTR)) = gc_end(group==Test group (TTR)) z = -0.131 Prob > |z| = 0.8956 . . . * 4.2.3 Satisfaction with received care . tab satisfaction_end group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Satisfaction with | Group allocation care | Control g Test grou | Total ----------------------+----------------------+---------- very satisfied | 24 24 | 48 | 70.59 75.00 | 72.73 ----------------------+----------------------+---------- fairly satisfied | 4 2 | 6 | 11.76 6.25 | 9.09 ----------------------+----------------------+---------- Neither satisfied nor | 1 3 | 4 | 2.94 9.38 | 6.06 ----------------------+----------------------+---------- Did not respond | 5 3 | 8 | 14.71 9.38 | 12.12 ----------------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(3) = 2.1080 Pr = 0.550 . ranksum satisfaction_end if(satisfaction_end!=6), by(group) Two-sample Wilcoxon rank-sum (Mann-Whitney) test group | obs rank sum expected -------------+--------------------------------- Control grou | 29 850.5 855.5 Test group ( | 29 860.5 855.5 -------------+--------------------------------- combined | 58 1711 1711 unadjusted variance 4134.92 adjustment for ties -2349.13 ---------- adjusted variance 1785.79 Ho: satisf~d(group==Control group (GTR)) = satisf~d(group==Test group (TTR)) z = -0.118 Prob > |z| = 0.9058 . . . * 4.2.5 Experience of care . tab experience_end group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Parent's experience | Group allocation of care | Control g Test grou | Total ---------------------+----------------------+---------- very good | 21 22 | 43 | 61.76 68.75 | 65.15 ---------------------+----------------------+---------- fairly good | 6 4 | 10 | 17.65 12.50 | 15.15 ---------------------+----------------------+---------- neither good nor bad | 2 2 | 4 | 5.88 6.25 | 6.06 ---------------------+----------------------+---------- fairly poor | 0 1 | 1 | 0.00 3.12 | 1.52 ---------------------+----------------------+---------- Did not respond | 5 3 | 8 | 14.71 9.38 | 12.12 ---------------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(4) = 1.8644 Pr = 0.761 . ranksum experience_end if(experience_end!=6), by(group) Two-sample Wilcoxon rank-sum (Mann-Whitney) test group | obs rank sum expected -------------+--------------------------------- Control grou | 29 864 855.5 Test group ( | 29 847 855.5 -------------+--------------------------------- combined | 58 1711 1711 unadjusted variance 4134.92 adjustment for ties -1706.80 ---------- adjusted variance 2428.11 Ho: experi~d(group==Control group (GTR)) = experi~d(group==Test group (TTR)) z = 0.172 Prob > |z| = 0.8630 . . . * 4.2.6 Unexpected reactions . tab ur_type1 group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Concerns | for more | Group allocation distress | Control g Test grou | Total -----------+----------------------+---------- No | 33 29 | 62 | 97.06 90.62 | 93.94 -----------+----------------------+---------- Yes | 1 2 | 3 | 2.94 6.25 | 4.55 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(2) = 1.5322 Pr = 0.465 . gen ur_type12=ur_type1 (1 missing value generated) . recode ur_type12 2=0 (ur_type12: 1 changes made) . tab ur_type12 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ur_type12 | Control g Test grou | Total -----------+----------------------+---------- 0 | 33 30 | 63 | 97.06 93.75 | 95.45 -----------+----------------------+---------- 1 | 1 2 | 3 | 2.94 6.25 | 4.55 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.608 1-sided Fisher's exact = 0.477 . . tab ur_type2 group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Concerns | for more | Group allocation crying | Control g Test grou | Total -----------+----------------------+---------- No | 31 27 | 58 | 91.18 84.38 | 87.88 -----------+----------------------+---------- Yes | 3 4 | 7 | 8.82 12.50 | 10.61 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(2) = 1.3594 Pr = 0.507 . gen ur_type22=ur_type2 (1 missing value generated) . recode ur_type22 2=0 (ur_type22: 1 changes made) . tab ur_type22 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ur_type22 | Control g Test grou | Total -----------+----------------------+---------- 0 | 31 28 | 59 | 91.18 87.50 | 89.39 -----------+----------------------+---------- 1 | 3 4 | 7 | 8.82 12.50 | 10.61 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.705 1-sided Fisher's exact = 0.465 . . tab ur_type3 group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Concerns | for more | Group allocation unsettled | Control g Test grou | Total -----------+----------------------+---------- No | 32 24 | 56 | 94.12 75.00 | 84.85 -----------+----------------------+---------- Yes | 2 7 | 9 | 5.88 21.88 | 13.64 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(2) = 4.8645 Pr = 0.088 . gen ur_type32=ur_type3 (1 missing value generated) . recode ur_type32 2=0 (ur_type32: 1 changes made) . tab ur_type32 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ur_type32 | Control g Test grou | Total -----------+----------------------+---------- 0 | 32 25 | 57 | 94.12 78.12 | 86.36 -----------+----------------------+---------- 1 | 2 7 | 9 | 5.88 21.88 | 13.64 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.079 1-sided Fisher's exact = 0.061 . . tab ur_type4 group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Concerns | for more | Group allocation vomiting | Control g Test grou | Total -----------+----------------------+---------- No | 33 31 | 64 | 97.06 96.88 | 96.97 -----------+----------------------+---------- Yes | 1 0 | 1 | 2.94 0.00 | 1.52 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(2) = 2.0037 Pr = 0.367 . gen ur_type42=ur_type4 (1 missing value generated) . recode ur_type42 2=0 (ur_type42: 1 changes made) . tab ur_type42 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ur_type42 | Control g Test grou | Total -----------+----------------------+---------- 0 | 33 32 | 65 | 97.06 100.00 | 98.48 -----------+----------------------+---------- 1 | 1 0 | 1 | 2.94 0.00 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.515 . . tab ur_type5 group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Concerns | for | difficulti | Group allocation es feeding | Control g Test grou | Total -----------+----------------------+---------- No | 33 30 | 63 | 97.06 93.75 | 95.45 -----------+----------------------+---------- Yes | 1 1 | 2 | 2.94 3.12 | 3.03 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(2) = 1.0832 Pr = 0.582 . gen ur_type52=ur_type5 (1 missing value generated) . recode ur_type52 2=0 (ur_type52: 1 changes made) . tab ur_type52 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ur_type52 | Control g Test grou | Total -----------+----------------------+---------- 0 | 33 31 | 64 | 97.06 96.88 | 96.97 -----------+----------------------+---------- 1 | 1 1 | 2 | 2.94 3.12 | 3.03 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.738 . . tab ur_type6 group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Concerns | for | difficulti | es | Group allocation sleeping | Control g Test grou | Total -----------+----------------------+---------- No | 33 30 | 63 | 97.06 93.75 | 95.45 -----------+----------------------+---------- Yes | 1 1 | 2 | 2.94 3.12 | 3.03 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(2) = 1.0832 Pr = 0.582 . gen ur_type62=ur_type6 (1 missing value generated) . recode ur_type62 2=0 (ur_type62: 1 changes made) . tab ur_type62 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ur_type62 | Control g Test grou | Total -----------+----------------------+---------- 0 | 33 31 | 64 | 97.06 96.88 | 96.97 -----------+----------------------+---------- 1 | 1 1 | 2 | 2.94 3.12 | 3.03 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.738 . . tab ur_type7 group, col chi2 +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Other | Group allocation concerns | Control g Test grou | Total -----------+----------------------+---------- No | 7 5 | 12 | 20.59 15.62 | 18.18 -----------+----------------------+---------- Yes | 16 13 | 29 | 47.06 40.62 | 43.94 -----------+----------------------+---------- Unknown | 11 14 | 25 | 32.35 43.75 | 37.88 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Pearson chi2(2) = 0.9439 Pr = 0.624 . gen ur_type72=ur_type7 (1 missing value generated) . recode ur_type72 2=0 (ur_type72: 25 changes made) . tab ur_type72 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ur_type72 | Control g Test grou | Total -----------+----------------------+---------- 0 | 18 19 | 37 | 52.94 59.38 | 56.06 -----------+----------------------+---------- 1 | 16 13 | 29 | 47.06 40.62 | 43.94 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.628 1-sided Fisher's exact = 0.391 . . tab ur_type7_txt if(group==1) Details for other concerns | Freq. Percent Cum. ----------------------------------------+----------------------------------- Answered no | 1 7.69 7.69 Difficult to wake due to improved, bu.. | 1 7.69 15.38 N/A | 3 23.08 38.46 N/a | 1 7.69 46.15 No, more settled | 1 7.69 53.85 None | 2 15.38 69.23 Not applicable | 1 7.69 76.92 Not straight away but treatment didn'.. | 1 7.69 84.62 Rien à signaler. | 1 7.69 92.31 “Other” means no, the questionnai.. | 1 7.69 100.00 ----------------------------------------+----------------------------------- Total | 13 100.00 . tab ur_type7_txt if(group==0) Details for other concerns | Freq. Percent Cum. ----------------------------------------+----------------------------------- N/a | 2 14.29 14.29 Na | 1 7.14 21.43 Nil | 1 7.14 28.57 Nil | 1 7.14 35.71 No concern. He continued to cry the s.. | 1 7.14 42.86 None | 2 14.29 57.14 None | 1 7.14 64.29 None, form would not let me continue .. | 1 7.14 71.43 Not crying as much - good days with l.. | 1 7.14 78.57 She has become fussy and crying when .. | 1 7.14 85.71 The first 24 hours he brought up a lo.. | 1 7.14 92.86 n/a | 1 7.14 100.00 ----------------------------------------+----------------------------------- Total | 14 100.00 . . // Only three other reactions were reported . cci 1 32 2 34, exact Proportion | Exposed Unexposed | Total Exposed -----------------+------------------------+------------------------ Cases | 1 32 | 33 0.0303 Controls | 2 34 | 36 0.0556 -----------------+------------------------+------------------------ Total | 3 66 | 69 0.0435 | | | Point estimate | [95% Conf. Interval] |------------------------+------------------------ Odds ratio | .53125 | .0087361 10.76343 (exact) Prev. frac. ex. | .46875 | -9.763427 .9912639 (exact) Prev. frac. pop | .0260417 | +------------------------------------------------- 1-sided Fisher's exact P = 0.5331 2-sided Fisher's exact P = 1.0000 . . . * 4.2.7 Serious adverse events . // To be added once dataset about SAEs is completed . . . * ---------------------- 5. PATIENT MANAGEMENT ------------------ . . * 5.1 Number of sessions . ttest visits, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- Control | 31 2.129032 .1589071 .8847574 1.804501 2.453564 Test gro | 32 2.5625 .1735394 .9816872 2.208564 2.916436 ---------+-------------------------------------------------------------------- combined | 63 2.349206 .1200758 .9530719 2.109178 2.589234 ---------+-------------------------------------------------------------------- diff | -.4334677 .2356963 -.9047717 .0378362 ------------------------------------------------------------------------------ diff = mean(Control) - mean(Test gro) t = -1.8391 Ho: diff = 0 degrees of freedom = 61 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0354 Pr(|T| > |t|) = 0.0708 Pr(T > t) = 0.9646 . mixed visits group Mixed-effects ML regression Number of obs = 63 Wald chi2(1) = 3.49 Log likelihood = -84.161151 Prob > chi2 = 0.0616 ------------------------------------------------------------------------------ visits | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | .4334677 .231925 1.87 0.062 -.0210968 .8880323 _cons | 2.129032 .1652921 12.88 0.000 1.805066 2.452999 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ var(Residual) | .8469662 .1509075 .5973157 1.200959 ------------------------------------------------------------------------------ . . tab visits group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 4: enumerations = 1 stage 3: enumerations = 9 stage 2: enumerations = 62 stage 1: enumerations = 0 Number of | Group allocation sessions | Control g Test grou | Total -----------+----------------------+---------- 1 | 6 5 | 11 | 19.35 15.62 | 17.46 -----------+----------------------+---------- 2 | 19 10 | 29 | 61.29 31.25 | 46.03 -----------+----------------------+---------- 3 | 2 11 | 13 | 6.45 34.38 | 20.63 -----------+----------------------+---------- 4 | 4 6 | 10 | 12.90 18.75 | 15.87 -----------+----------------------+---------- Total | 31 32 | 63 | 100.00 100.00 | 100.00 Fisher's exact = 0.020 . . . * 5.2 Advice . tab ad_sleep group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 4 stage 1: enumerations = 0 Advice – | Managing the | baby’s | sleep | Group allocation pattern | Control g Test grou | Total -------------+----------------------+---------- No | 20 23 | 43 | 58.82 71.88 | 65.15 -------------+----------------------+---------- Yes | 9 9 | 18 | 26.47 28.12 | 27.27 -------------+----------------------+---------- Not reported | 5 0 | 5 | 14.71 0.00 | 7.58 -------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.090 . gen ad_sleep2=ad_sleep . recode ad_sleep2 2=0 (ad_sleep2: 5 changes made) . tab ad_sleep2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ad_sleep2 | Control g Test grou | Total -----------+----------------------+---------- 0 | 25 23 | 48 | 73.53 71.88 | 72.73 -----------+----------------------+---------- 1 | 9 9 | 18 | 26.47 28.12 | 27.27 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.549 . . tab ad_feeding group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 4 stage 1: enumerations = 0 Advice – | Managing the | baby’s | feeding | Group allocation pattern | Control g Test grou | Total -------------+----------------------+---------- No | 19 18 | 37 | 55.88 56.25 | 56.06 -------------+----------------------+---------- Yes | 11 14 | 25 | 32.35 43.75 | 37.88 -------------+----------------------+---------- Not reported | 4 0 | 4 | 11.76 0.00 | 6.06 -------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.165 . gen ad_feeding2=ad_feeding . recode ad_feeding2 2=0 (ad_feeding2: 4 changes made) . tab ad_feeding2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ ad_feeding | Group allocation 2 | Control g Test grou | Total -----------+----------------------+---------- 0 | 23 18 | 41 | 67.65 56.25 | 62.12 -----------+----------------------+---------- 1 | 11 14 | 25 | 32.35 43.75 | 37.88 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.447 1-sided Fisher's exact = 0.242 . . tab ad_diet group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 5 stage 1: enumerations = 0 Advice – | Mother’s | Group allocation diet | Control g Test grou | Total -------------+----------------------+---------- No | 20 17 | 37 | 58.82 53.12 | 56.06 -------------+----------------------+---------- Yes | 10 15 | 25 | 29.41 46.88 | 37.88 -------------+----------------------+---------- Not reported | 4 0 | 4 | 11.76 0.00 | 6.06 -------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.068 . gen ad_diet2=ad_diet . recode ad_diet2 2=0 (ad_diet2: 4 changes made) . tab ad_diet2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ad_diet2 | Control g Test grou | Total -----------+----------------------+---------- 0 | 24 17 | 41 | 70.59 53.12 | 62.12 -----------+----------------------+---------- 1 | 10 15 | 25 | 29.41 46.88 | 37.88 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.205 1-sided Fisher's exact = 0.113 . . tab ad_handling group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 2 stage 1: enumerations = 0 Advice – | Handling the | Group allocation baby | Control g Test grou | Total -------------+----------------------+---------- No | 14 15 | 29 | 41.18 46.88 | 43.94 -------------+----------------------+---------- Yes | 17 17 | 34 | 50.00 53.12 | 51.52 -------------+----------------------+---------- Not reported | 3 0 | 3 | 8.82 0.00 | 4.55 -------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.340 . gen ad_handling2=ad_handling . recode ad_handling2 2=0 (ad_handling2: 3 changes made) . tab ad_handling2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ ad_handlin | Group allocation g2 | Control g Test grou | Total -----------+----------------------+---------- 0 | 17 15 | 32 | 50.00 46.88 | 48.48 -----------+----------------------+---------- 1 | 17 17 | 34 | 50.00 53.12 | 51.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.811 1-sided Fisher's exact = 0.497 . . tab ad_env group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 4 stage 1: enumerations = 0 Advice – | Managing the | baby’s | Group allocation environment | Control g Test grou | Total -------------+----------------------+---------- No | 20 24 | 44 | 58.82 75.00 | 66.67 -------------+----------------------+---------- Yes | 10 8 | 18 | 29.41 25.00 | 27.27 -------------+----------------------+---------- Not reported | 4 0 | 4 | 11.76 0.00 | 6.06 -------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.141 . gen ad_env2=ad_env . recode ad_env2 2=0 (ad_env2: 4 changes made) . tab ad_env2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ad_env2 | Control g Test grou | Total -----------+----------------------+---------- 0 | 24 24 | 48 | 70.59 75.00 | 72.73 -----------+----------------------+---------- 1 | 10 8 | 18 | 29.41 25.00 | 27.27 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.785 1-sided Fisher's exact = 0.451 . . tab ad_beh group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 5 stage 1: enumerations = 0 Advice – | Parenting | Group allocation behaviour | Control g Test grou | Total -------------+----------------------+---------- No | 18 23 | 41 | 52.94 71.88 | 62.12 -------------+----------------------+---------- Yes | 12 9 | 21 | 35.29 28.12 | 31.82 -------------+----------------------+---------- Not reported | 4 0 | 4 | 11.76 0.00 | 6.06 -------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.092 . gen ad_beh2=ad_beh . recode ad_beh2 2=0 (ad_beh2: 4 changes made) . tab ad_beh2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ad_beh2 | Control g Test grou | Total -----------+----------------------+---------- 0 | 22 23 | 45 | 64.71 71.88 | 68.18 -----------+----------------------+---------- 1 | 12 9 | 21 | 35.29 28.12 | 31.82 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.603 1-sided Fisher's exact = 0.360 . . tab ad_reas group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 4 stage 1: enumerations = 0 Advice – | Reassure | parent/guard | Group allocation ian | Control g Test grou | Total -------------+----------------------+---------- No | 1 3 | 4 | 2.94 9.38 | 6.06 -------------+----------------------+---------- Yes | 30 29 | 59 | 88.24 90.62 | 89.39 -------------+----------------------+---------- Not reported | 3 0 | 3 | 8.82 0.00 | 4.55 -------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.234 . gen ad_reas2=ad_reas . recode ad_reas2 2=0 (ad_reas2: 3 changes made) . tab ad_reas2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ad_reas2 | Control g Test grou | Total -----------+----------------------+---------- 0 | 4 3 | 7 | 11.76 9.38 | 10.61 -----------+----------------------+---------- 1 | 30 29 | 59 | 88.24 90.62 | 89.39 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.535 . . tab ad_other group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 4 stage 1: enumerations = 0 Advice – | Group allocation Other | Control g Test grou | Total -------------+----------------------+---------- No | 19 18 | 37 | 55.88 56.25 | 56.06 -------------+----------------------+---------- Yes | 11 14 | 25 | 32.35 43.75 | 37.88 -------------+----------------------+---------- Not reported | 4 0 | 4 | 11.76 0.00 | 6.06 -------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.165 . gen ad_other2=ad_other . recode ad_other2 2=0 (ad_other2: 4 changes made) . tab ad_other2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation ad_other2 | Control g Test grou | Total -----------+----------------------+---------- 0 | 23 18 | 41 | 67.65 56.25 | 62.12 -----------+----------------------+---------- 1 | 11 14 | 25 | 32.35 43.75 | 37.88 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.447 1-sided Fisher's exact = 0.242 . . . * 5.3 Medication and Remedies . tab pharm1 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 2 stage 1: enumerations = 0 Pharm & | supplement | s - | Prescribed | Group allocation Medicine | Control g Test grou | Total -----------+----------------------+---------- No | 31 26 | 57 | 91.18 81.25 | 86.36 -----------+----------------------+---------- Yes | 3 5 | 8 | 8.82 15.62 | 12.12 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.357 . gen pharm12=pharm1 (1 missing value generated) . recode pharm12 2=0 (pharm12: 1 changes made) . tab pharm12 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation pharm12 | Control g Test grou | Total -----------+----------------------+---------- 0 | 31 27 | 58 | 91.18 84.38 | 87.88 -----------+----------------------+---------- 1 | 3 5 | 8 | 8.82 15.62 | 12.12 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.469 1-sided Fisher's exact = 0.320 . . tab pharm2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 2 stage 1: enumerations = 0 Pharm & | supplement | s - | Anti-gas | Group allocation drops | Control g Test grou | Total -----------+----------------------+---------- No | 23 26 | 49 | 67.65 81.25 | 74.24 -----------+----------------------+---------- Yes | 11 5 | 16 | 32.35 15.62 | 24.24 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.154 . gen pharm22=pharm2 (1 missing value generated) . recode pharm22 2=0 (pharm22: 1 changes made) . tab pharm22 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation pharm22 | Control g Test grou | Total -----------+----------------------+---------- 0 | 23 27 | 50 | 67.65 84.38 | 75.76 -----------+----------------------+---------- 1 | 11 5 | 16 | 32.35 15.62 | 24.24 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.154 1-sided Fisher's exact = 0.097 . . tab pharm3 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Pharm & | supplement | s - Herbal | supplement | Group allocation s | Control g Test grou | Total -----------+----------------------+---------- No | 34 31 | 65 | 100.00 96.88 | 98.48 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.485 1-sided Fisher's exact = 0.485 . gen pharm32=pharm3 (1 missing value generated) . recode pharm32 2=0 (pharm32: 1 changes made) . tab pharm32 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation pharm32 | Control g Test grou | Total -----------+----------------------+---------- 0 | 34 32 | 66 | 100.00 100.00 | 100.00 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 . . tab pharm4 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 1 stage 1: enumerations = 0 Pharm & | supplement | s - | Group allocation Probiotics | Control g Test grou | Total -----------+----------------------+---------- No | 28 25 | 53 | 82.35 78.12 | 80.30 -----------+----------------------+---------- Yes | 6 6 | 12 | 17.65 18.75 | 18.18 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.872 . gen pharm42=pharm4 (1 missing value generated) . recode pharm42 2=0 (pharm42: 1 changes made) . tab pharm42 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation pharm42 | Control g Test grou | Total -----------+----------------------+---------- 0 | 28 26 | 54 | 82.35 81.25 | 81.82 -----------+----------------------+---------- 1 | 6 6 | 12 | 17.65 18.75 | 18.18 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.579 . . tab pharm5 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 2 stage 1: enumerations = 0 Pharm & | supplement | s - | Non-cow or | anti-aller | genic | formula | Group allocation drink | Control g Test grou | Total -----------+----------------------+---------- No | 33 25 | 58 | 97.06 78.12 | 87.88 -----------+----------------------+---------- Yes | 1 6 | 7 | 2.94 18.75 | 10.61 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.029 . gen pharm52=pharm5 (1 missing value generated) . recode pharm52 2=0 (pharm52: 1 changes made) . tab pharm52 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation pharm52 | Control g Test grou | Total -----------+----------------------+---------- 0 | 33 26 | 59 | 97.06 81.25 | 89.39 -----------+----------------------+---------- 1 | 1 6 | 7 | 2.94 18.75 | 10.61 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.051 1-sided Fisher's exact = 0.044 . . tab pharm6 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 1 stage 1: enumerations = 0 Pharm & | supplement | s - | Group allocation Omeprazol | Control g Test grou | Total -----------+----------------------+---------- No | 34 29 | 63 | 100.00 90.62 | 95.45 -----------+----------------------+---------- Yes | 0 2 | 2 | 0.00 6.25 | 3.03 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.108 . gen pharm62=pharm6 (1 missing value generated) . recode pharm62 2=0 (pharm62: 1 changes made) . tab pharm62 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation pharm62 | Control g Test grou | Total -----------+----------------------+---------- 0 | 34 30 | 64 | 100.00 93.75 | 96.97 -----------+----------------------+---------- 1 | 0 2 | 2 | 0.00 6.25 | 3.03 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.231 1-sided Fisher's exact = 0.231 . . tab pharm7 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 2 stage 1: enumerations = 0 Pharm & | supplement | Group allocation s - Other | Control g Test grou | Total -----------+----------------------+---------- No | 22 19 | 41 | 64.71 59.38 | 62.12 -----------+----------------------+---------- Yes | 12 12 | 24 | 35.29 37.50 | 36.36 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.704 . gen pharm72=pharm7 (1 missing value generated) . recode pharm72 2=0 (pharm72: 1 changes made) . tab pharm72 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ | Group allocation pharm72 | Control g Test grou | Total -----------+----------------------+---------- 0 | 22 20 | 42 | 64.71 62.50 | 63.64 -----------+----------------------+---------- 1 | 12 12 | 24 | 35.29 37.50 | 36.36 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.527 . . tab pharm7_txt if(group==1) Details for other Medicine or Remedies | Freq. Percent Cum. ----------------------------------------+----------------------------------- Gaviscon | 2 16.67 16.67 Gaviscon (already prescribed before t.. | 1 8.33 25.00 Gavison infant | 1 8.33 33.33 I got prescribed omeprazole but didn'.. | 1 8.33 41.67 Infacol | 1 8.33 50.00 N/A | 1 8.33 58.33 No other medication. | 1 8.33 66.67 None | 1 8.33 75.00 Nothing | 1 8.33 83.33 Prescribed medication was for an eye .. | 1 8.33 91.67 Willbys colic drops | 1 8.33 100.00 ----------------------------------------+----------------------------------- Total | 12 100.00 . tab pharm7_txt if(group==0) Details for other Medicine or Remedies | Freq. Percent Cum. ----------------------------------------+----------------------------------- Colief | 1 10.00 10.00 Gripe Water Calpol - Vaccinations | 1 10.00 20.00 Infacol | 1 10.00 30.00 Infant gaviscon | 1 10.00 40.00 Na | 1 10.00 50.00 Nil | 1 10.00 60.00 No - considered giving anti gas drops.. | 1 10.00 70.00 None | 2 20.00 90.00 Probiotic | 1 10.00 100.00 ----------------------------------------+----------------------------------- Total | 10 100.00 . . * 5.4 Additional care . tab other_care group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 3 stage 1: enumerations = 0 | Group allocation Other care | Control g Test grou | Total ----------------------+----------------------+---------- Received other care | 22 21 | 43 | 64.71 65.62 | 65.15 ----------------------+----------------------+---------- Did not receive other | 5 2 | 7 | 14.71 6.25 | 10.61 ----------------------+----------------------+---------- Unknown if other care | 7 9 | 16 | 20.59 28.12 | 24.24 ----------------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.529 . gen other_care_2=other_care (1 missing value generated) . recode other_care_2 2/3=0 (other_care_2: 23 changes made) . tab other_care_2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ other_care | Group allocation _2 | Control g Test grou | Total -----------+----------------------+---------- 0 | 12 11 | 23 | 35.29 34.38 | 34.85 -----------+----------------------+---------- 1 | 22 21 | 43 | 64.71 65.62 | 65.15 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.572 . . tab other_care1 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 2 stage 1: enumerations = 0 Other care | Group allocation - GP | Control g Test grou | Total -----------+----------------------+---------- No | 28 23 | 51 | 82.35 71.88 | 77.27 -----------+----------------------+---------- Yes | 6 8 | 14 | 17.65 25.00 | 21.21 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.455 . gen other_care12=other_care1 (1 missing value generated) . recode other_care12 2/3=0 (other_care12: 1 changes made) . tab other_care12 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ other_care | Group allocation 12 | Control g Test grou | Total -----------+----------------------+---------- 0 | 28 24 | 52 | 82.35 75.00 | 78.79 -----------+----------------------+---------- 1 | 6 8 | 14 | 17.65 25.00 | 21.21 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.554 1-sided Fisher's exact = 0.334 . . tab other_care2 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Other care | Group allocation - A&E | Control g Test grou | Total -----------+----------------------+---------- No | 34 31 | 65 | 100.00 96.88 | 98.48 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.485 1-sided Fisher's exact = 0.485 . gen other_care22=other_care2 (1 missing value generated) . recode other_care22 2/3=0 (other_care22: 1 changes made) . tab other_care22 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ other_care | Group allocation 22 | Control g Test grou | Total -----------+----------------------+---------- 0 | 34 32 | 66 | 100.00 100.00 | 100.00 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 . . tab other_care3 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 1 stage 1: enumerations = 0 Other care | - | Unplanned | hospital | Group allocation admission | Control g Test grou | Total -----------+----------------------+---------- No | 33 31 | 64 | 97.06 96.88 | 96.97 -----------+----------------------+---------- Yes | 1 0 | 1 | 2.94 0.00 | 1.52 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.738 . gen other_care32=other_care3 (1 missing value generated) . recode other_care32 2/3=0 (other_care32: 1 changes made) . tab other_care32 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ other_care | Group allocation 32 | Control g Test grou | Total -----------+----------------------+---------- 0 | 33 32 | 65 | 97.06 100.00 | 98.48 -----------+----------------------+---------- 1 | 1 0 | 1 | 2.94 0.00 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.515 . . tab other_care4 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 1 stage 1: enumerations = 0 Other care | - Other | Group allocation osteopath | Control g Test grou | Total -----------+----------------------+---------- No | 33 30 | 63 | 97.06 93.75 | 95.45 -----------+----------------------+---------- Yes | 1 1 | 2 | 2.94 3.12 | 3.03 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.738 . gen other_care42=other_care4 (1 missing value generated) . recode other_care42 2/3=0 (other_care42: 1 changes made) . tab other_care42 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ other_care | Group allocation 42 | Control g Test grou | Total -----------+----------------------+---------- 0 | 33 31 | 64 | 97.06 96.88 | 96.97 -----------+----------------------+---------- 1 | 1 1 | 2 | 2.94 3.12 | 3.03 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.738 . . tab other_care5 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 1 stage 1: enumerations = 0 Other care | - | Paediatric | Group allocation ian | Control g Test grou | Total -----------+----------------------+---------- No | 34 26 | 60 | 100.00 81.25 | 90.91 -----------+----------------------+---------- Yes | 0 5 | 5 | 0.00 15.62 | 7.58 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.010 . gen other_care52=other_care5 (1 missing value generated) . recode other_care52 2/3=0 (other_care52: 1 changes made) . tab other_care52 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ other_care | Group allocation 52 | Control g Test grou | Total -----------+----------------------+---------- 0 | 34 27 | 61 | 100.00 84.38 | 92.42 -----------+----------------------+---------- 1 | 0 5 | 5 | 0.00 15.62 | 7.58 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.023 1-sided Fisher's exact = 0.023 . . tab other_care6 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 1 stage 1: enumerations = 0 Other care | - | Pharmacist | Group allocation n | Control g Test grou | Total -----------+----------------------+---------- No | 33 31 | 64 | 97.06 96.88 | 96.97 -----------+----------------------+---------- Yes | 1 0 | 1 | 2.94 0.00 | 1.52 -----------+----------------------+---------- Unknown | 0 1 | 1 | 0.00 3.12 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.738 . gen other_care62=other_care6 (1 missing value generated) . recode other_care62 2/3=0 (other_care62: 1 changes made) . tab other_care62 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ other_care | Group allocation 62 | Control g Test grou | Total -----------+----------------------+---------- 0 | 33 32 | 65 | 97.06 100.00 | 98.48 -----------+----------------------+---------- 1 | 1 0 | 1 | 2.94 0.00 | 1.52 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.515 . . tab other_care7 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 1 stage 1: enumerations = 0 Other care | - | Lactation | consultant | Group allocation or midwife | Control g Test grou | Total -----------+----------------------+---------- No | 22 17 | 39 | 64.71 53.12 | 59.09 -----------+----------------------+---------- Yes | 1 1 | 2 | 2.94 3.12 | 3.03 -----------+----------------------+---------- Unknown | 11 14 | 25 | 32.35 43.75 | 37.88 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.717 . gen other_care72=other_care7 (1 missing value generated) . recode other_care72 2/3=0 (other_care72: 25 changes made) . tab other_care72 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ other_care | Group allocation 72 | Control g Test grou | Total -----------+----------------------+---------- 0 | 33 31 | 64 | 97.06 96.88 | 96.97 -----------+----------------------+---------- 1 | 1 1 | 2 | 2.94 3.12 | 3.03 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 1.000 1-sided Fisher's exact = 0.738 . . tab other_care8 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 3 stage 1: enumerations = 0 Other care | Group allocation - Other | Control g Test grou | Total -----------+----------------------+---------- No | 16 22 | 38 | 47.06 68.75 | 57.58 -----------+----------------------+---------- Yes | 17 9 | 26 | 50.00 28.12 | 39.39 -----------+----------------------+---------- Unknown | 1 1 | 2 | 2.94 3.12 | 3.03 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.141 . gen other_care82=other_care8 (1 missing value generated) . recode other_care82 2/3=0 (other_care82: 2 changes made) . tab other_care82 group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ other_care | Group allocation 82 | Control g Test grou | Total -----------+----------------------+---------- 0 | 17 23 | 40 | 50.00 71.88 | 60.61 -----------+----------------------+---------- 1 | 17 9 | 26 | 50.00 28.12 | 39.39 -----------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.083 1-sided Fisher's exact = 0.058 . . tab other_care8_txt if(group==1) Details for other care | Freq. Percent Cum. ----------------------------------------+----------------------------------- I sought support from a lactation con.. | 1 14.29 14.29 N/a | 1 14.29 28.57 No additional care needed | 1 14.29 42.86 No additional care. | 1 14.29 57.14 None | 2 28.57 85.71 Tongue tie specialist | 1 14.29 100.00 ----------------------------------------+----------------------------------- Total | 7 100.00 . tab other_care8_txt if(group==0) Details for other care | Freq. Percent Cum. ----------------------------------------+----------------------------------- Didn’t see anyone else | 1 8.33 8.33 Emma has been attending osteopath at .. | 1 8.33 16.67 Lactation consultant visited on day 7.. | 1 8.33 25.00 Maternal child health nurse | 1 8.33 33.33 Nil | 1 8.33 41.67 Nil | 1 8.33 50.00 None | 3 25.00 75.00 None, form would not let me continue .. | 1 8.33 83.33 Routine immunisation | 1 8.33 91.67 Went to GP two days after first treat.. | 1 8.33 100.00 ----------------------------------------+----------------------------------- Total | 12 100.00 . . . * 5.5 Blinding . tab allocation_guess group, col exact +-------------------+ | Key | |-------------------| | frequency | | column percentage | +-------------------+ Enumerating sample-space combinations: stage 4: enumerations = 1 stage 3: enumerations = 3 stage 2: enumerations = 8 stage 1: enumerations = 0 Group allocation | Group allocation guess | Control g Test grou | Total ----------------------+----------------------+---------- Control treatment (TT | 6 5 | 11 | 17.65 15.62 | 16.67 ----------------------+----------------------+---------- Test treatment (GTR) | 4 7 | 11 | 11.76 21.88 | 16.67 ----------------------+----------------------+---------- Don’t know/unsure | 19 17 | 36 | 55.88 53.12 | 54.55 ----------------------+----------------------+---------- Did not respond | 5 3 | 8 | 14.71 9.38 | 12.12 ----------------------+----------------------+---------- Total | 34 32 | 66 | 100.00 100.00 | 100.00 Fisher's exact = 0.730 . . * 5.5.1 Updating dataset file . . save source_files/dataset_wide.dta, replace file source_files/dataset_wide.dta saved . reshape long d, i(id) j(day) (note: j = 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14) Data wide -> long ----------------------------------------------------------------------------- Number of obs. 67 -> 1005 Number of variables 116 -> 103 j variable (15 values) -> day xij variables: d0 d1 ... d14 -> d ----------------------------------------------------------------------------- . rename d crying_time . . . gen ct_ln = ln(2.718+crying_time) (203 missing values generated) . gen baseline_ln = ln(2.718+baseline) (15 missing values generated) . . . label variable crying_time "Daily crying time (minutes)" . label variable day "Days within trial" . . drop _merge . . save Source_files/dataset.dta, replace file Source_files/dataset.dta saved . . * 5.6 Provided osteopathic manual treatment . . use source_files/omt.dta, clear . sum ttt_jart ttt_blt ttt_btt_c ttt_btt_tpl ttt_btt_v ttt_btt_0 ttt_cff /// > ttt_pff ttt_lfm ttt_count ttt_ind ttt_myor ttt_st ttt_visc ttt_other Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- ttt_jart | 32 .34375 .4825587 0 1 ttt_blt | 32 .65625 .4825587 0 1 ttt_btt_c | 32 .84375 .368902 0 1 ttt_btt_tpl | 32 .65625 .4825587 0 1 ttt_btt_v | 32 .65625 .4825587 0 1 -------------+--------------------------------------------------------- ttt_btt_0 | 32 .25 .4399413 0 1 ttt_cff | 32 .375 .4918694 0 1 ttt_pff | 32 0 0 0 0 ttt_lfm | 32 .03125 .1767767 0 1 ttt_count | 32 .15625 .368902 0 1 -------------+--------------------------------------------------------- ttt_ind | 32 .4375 .5040161 0 1 ttt_myor | 32 .3125 .4709291 0 1 ttt_st | 32 .40625 .4989909 0 1 ttt_visc | 32 .28125 .4568034 0 1 ttt_other | 32 .3125 .4709291 0 1 . . tab ttt_btt_c (max) | ttt_btt_c | Freq. Percent Cum. ------------+----------------------------------- 0 | 5 15.62 15.62 1 | 27 84.38 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_btt_tpl (max) | ttt_btt_tpl | Freq. Percent Cum. ------------+----------------------------------- 0 | 11 34.38 34.38 1 | 21 65.62 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_btt_v (max) | ttt_btt_v | Freq. Percent Cum. ------------+----------------------------------- 0 | 11 34.38 34.38 1 | 21 65.62 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_blt (max) | ttt_blt | Freq. Percent Cum. ------------+----------------------------------- 0 | 11 34.38 34.38 1 | 21 65.62 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_ind (max) | ttt_ind | Freq. Percent Cum. ------------+----------------------------------- 0 | 18 56.25 56.25 1 | 14 43.75 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_st (max) | ttt_st | Freq. Percent Cum. ------------+----------------------------------- 0 | 19 59.38 59.38 1 | 13 40.62 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_cff (max) | ttt_cff | Freq. Percent Cum. ------------+----------------------------------- 0 | 20 62.50 62.50 1 | 12 37.50 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_jart (max) | ttt_jart | Freq. Percent Cum. ------------+----------------------------------- 0 | 21 65.62 65.62 1 | 11 34.38 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_myor (max) | ttt_myor | Freq. Percent Cum. ------------+----------------------------------- 0 | 22 68.75 68.75 1 | 10 31.25 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_visc (max) | ttt_visc | Freq. Percent Cum. ------------+----------------------------------- 0 | 23 71.88 71.88 1 | 9 28.12 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_btt_0 (max) | ttt_btt_0 | Freq. Percent Cum. ------------+----------------------------------- 0 | 24 75.00 75.00 1 | 8 25.00 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_count (max) | ttt_count | Freq. Percent Cum. ------------+----------------------------------- 0 | 27 84.38 84.38 1 | 5 15.62 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_lfm (max) | ttt_lfm | Freq. Percent Cum. ------------+----------------------------------- 0 | 31 96.88 96.88 1 | 1 3.12 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_pff (max) | ttt_pff | Freq. Percent Cum. ------------+----------------------------------- 0 | 32 100.00 100.00 ------------+----------------------------------- Total | 32 100.00 . tab ttt_other (max) | ttt_other | Freq. Percent Cum. ------------+----------------------------------- 0 | 22 68.75 68.75 1 | 10 31.25 100.00 ------------+----------------------------------- Total | 32 100.00 . . . . * ---------------------- 6. SENSITIVITY ANALYSIS ------------------ . . * 6.1 Per protocol analysis (only analysing patients who received the allocated treatment) . use source_files/dataset.dta, clear . mixed crying_time group baseline expectation2 age_cat if(day>1 & perprotocol==1) || id:, var reml // adjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3534.2085 Iteration 1: log restricted-likelihood = -3534.2085 Computing standard errors: Mixed-effects REML regression Number of obs = 638 Group variable: id Number of groups = 51 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(4) = 49.99 Log restricted-likelihood = -3534.2085 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 4.425122 12.27604 0.36 0.718 -19.63547 28.48571 baseline | .3719717 .0619952 6.00 0.000 .2504633 .4934801 expectation2 | -5.754403 5.666272 -1.02 0.310 -16.86009 5.351286 age_cat | -17.95633 6.289632 -2.85 0.004 -30.28379 -5.628883 _cons | 85.06655 24.42774 3.48 0.000 37.18905 132.944 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 1617.815 396.2544 1001.019 2614.66 -----------------------------+------------------------------------------------ var(Residual) | 3374.589 197.0424 3009.672 3783.751 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 135.90 Prob >= chibar2 = 0.0000 . . * 6.2 Log transformed analysis . mixed ct_ln group baseline_ln expectation2 age_cat if(day>1 & perprotocol==1) || id:, var reml // adjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -616.90007 Iteration 1: log restricted-likelihood = -616.90007 Computing standard errors: Mixed-effects REML regression Number of obs = 638 Group variable: id Number of groups = 51 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(4) = 34.54 Log restricted-likelihood = -616.90007 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ ct_ln | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | .0121352 .1372122 0.09 0.930 -.2567958 .2810662 baseline_ln | .6609049 .1381707 4.78 0.000 .3900952 .9317146 expectation2 | -.0313673 .062726 -0.50 0.617 -.1543079 .0915733 age_cat | -.2345838 .0704695 -3.33 0.001 -.3727014 -.0964661 _cons | 1.625861 .7346255 2.21 0.027 .1860217 3.065701 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | .2108052 .0497465 .132743 .3347733 -----------------------------+------------------------------------------------ var(Residual) | .3349087 .0195523 .298698 .3755091 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 183.04 Prob >= chibar2 = 0.0000 . . . * 6.3 Analysis without crying time imputation . mixed crying_time group baseline expectation2 age_cat if(day>1 & id!=6104003 & id!=4101011 & id!=4403014) || id:, var reml // adjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3605.8481 Iteration 1: log restricted-likelihood = -3605.8481 Computing standard errors: Mixed-effects REML regression Number of obs = 651 Group variable: id Number of groups = 52 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(4) = 51.22 Log restricted-likelihood = -3605.8481 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 3.602985 12.09477 0.30 0.766 -20.10233 27.3083 baseline | .3713206 .061519 6.04 0.000 .2507457 .4918956 expectation2 | -6.157172 5.577921 -1.10 0.270 -17.0897 4.775352 age_cat | -18.71919 6.092348 -3.07 0.002 -30.65997 -6.778409 _cons | 88.77931 23.32413 3.81 0.000 43.06486 134.4938 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 1589.816 386.1654 987.6217 2559.195 -----------------------------+------------------------------------------------ var(Residual) | 3371.231 194.867 3010.139 3775.638 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 136.38 Prob >= chibar2 = 0.0000 . . * 6.4 Adjusting for eventual attrition bias . mixed crying_time group baseline expectation2 age_cat pharm52 other_care52 i.visit if(day>1) || id:, var reml // adjusted Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -3638.6697 Iteration 1: log restricted-likelihood = -3638.6697 Computing standard errors: Mixed-effects REML regression Number of obs = 661 Group variable: id Number of groups = 53 Obs per group: min = 7 avg = 12.5 max = 13 Wald chi2(9) = 48.29 Log restricted-likelihood = -3638.6697 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 7.55681 14.28026 0.53 0.597 -20.43198 35.5456 baseline | .3832861 .0653578 5.86 0.000 .2551871 .511385 expectation2 | -6.139149 5.774036 -1.06 0.288 -17.45605 5.177754 age_cat | -17.22014 6.369285 -2.70 0.007 -29.70371 -4.736568 pharm52 | -25.45592 27.55243 -0.92 0.356 -79.45769 28.54585 other_care52 | 22.36394 27.4272 0.82 0.415 -31.39238 76.12026 | visits | 2 | 21.34081 20.54496 1.04 0.299 -18.92657 61.60819 3 | 10.153 23.54172 0.43 0.666 -35.98793 56.29392 4 | 20.35175 23.01647 0.88 0.377 -24.75971 65.46321 | _cons | 63.29307 27.91882 2.27 0.023 8.573182 118.013 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 1643.871 415.3227 1001.872 2697.262 -----------------------------+------------------------------------------------ var(Residual) | 3356.232 192.5485 2999.288 3755.656 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 132.62 Prob >= chibar2 = 0.0000 . . * 6.5 Between institution differences . . // https://www.stata.com/meeting/brazil17/slides/brazil17_Favero.pdf . // Multilevel Mixed-Effects Linear Models . . meglm crying_time group baseline expectation2 age_cat || inst: || id: , family(gaussian) link(identity) nolog Mixed-effects GLM Number of obs = 802 Family: Gaussian Link: identity ------------------------------------------------------------- | No. of Observations per Group Group Variable | Groups Minimum Average Maximum ----------------+-------------------------------------------- inst | 5 46 160.4 419 id | 66 1 12.2 15 ------------------------------------------------------------- Integration method: mvaghermite Integration pts. = 7 Wald chi2(4) = 92.98 Log likelihood = -4559.9282 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------------+---------------------------------------------------------------- group | 2.666783 10.77326 0.25 0.804 -18.44841 23.78198 baseline | .4904787 .0550799 8.90 0.000 .3825241 .5984334 expectation2 | -6.242271 5.027132 -1.24 0.214 -16.09527 3.610726 age_cat | -14.2117 5.465645 -2.60 0.009 -24.92417 -3.49923 _cons | 68.13547 20.23407 3.37 0.001 28.47742 107.7935 -------------------+---------------------------------------------------------------- inst | var(_cons)| 1.17e-32 1.58e-16 . . -------------------+---------------------------------------------------------------- inst>id | var(_cons)| 1346.741 366.0528 790.5369 2294.279 -------------------+---------------------------------------------------------------- var(e.crying_time)| 4518.105 237.8112 4075.24 5009.097 ------------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 87.16 Prob >= chibar2 = 0.0000 . save source_files/dataset.dta, replace file source_files/dataset.dta saved . . . . * 6.6 Worse case scenario . * 6.5.1 Preparing data (computing percentiles) . egen avg = mean(crying_time) if(day>1), by(id) (290 missing values generated) . sum avg if(group==1), detail avg ------------------------------------------------------------- Percentiles Smallest 1% 28.92308 28.92308 5% 33.38462 28.92308 10% 45.71429 28.92308 Obs 338 25% 75.2 28.92308 Sum of Wgt. 338 50% 102.1923 Mean 123.5686 Largest Std. Dev. 67.48912 75% 168.8462 309.0769 90% 218.4615 309.0769 Variance 4554.782 95% 239.25 309.0769 Skewness .8931704 99% 309.0769 309.0769 Kurtosis 3.281976 . gen pct25_1=r(p25) . gen pct75_1=r(p75) . sum avg if(group==0), detail avg ------------------------------------------------------------- Percentiles Smallest 1% 26 26 5% 41.61538 26 10% 52.77778 26 Obs 377 25% 83.9 26 Sum of Wgt. 377 50% 103.4615 Mean 115.0022 Largest Std. Dev. 48.74095 75% 159.2308 198.4615 90% 185 198.4615 Variance 2375.68 95% 189.0769 198.4615 Skewness .1264211 99% 198.4615 198.4615 Kurtosis 1.863578 . gen pct25_0=r(p25) . gen pct75_0=r(p75) . . collapse (mean) inst group avg pct25_1 pct75_1 pct25_0 pct75_0 , by(id) . drop if group==. (1 observation deleted) . . save Temporary_files/wbscenario.dta, replace file Temporary_files/wbscenario.dta saved . . * 6.5.2 Imputing values . use Temporary_files/wbscenario.dta, clear . . replace avg=pct75_1 if(group==1 & avg==.) (6 real changes made) . replace avg=pct25_0 if(group==0 & avg==.) (5 real changes made) . . ttest avg, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- 0 | 34 110.4283 8.057138 46.98079 94.03593 126.8207 1 | 32 132.0581 11.36225 64.27461 108.8847 155.2316 ---------+-------------------------------------------------------------------- combined | 66 120.9155 6.97344 56.65249 106.9886 134.8424 ---------+-------------------------------------------------------------------- diff | -21.62982 13.7995 -49.19749 5.937855 ------------------------------------------------------------------------------ diff = mean(0) - mean(1) t = -1.5674 Ho: diff = 0 degrees of freedom = 64 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0610 Pr(|T| > |t|) = 0.1219 Pr(T > t) = 0.9390 . . * 6.7 Best case scenario . use Temporary_files/wbscenario.dta, clear . . replace avg=pct25_1 if(group==1 & avg==.) (6 real changes made) . replace avg=pct75_0 if(group==0 & avg==.) (5 real changes made) . . ttest avg, by(group) Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- 0 | 34 121.5064 8.287098 48.32167 104.6461 138.3666 1 | 32 114.4995 11.42468 64.62775 91.19868 137.8003 ---------+-------------------------------------------------------------------- combined | 66 118.1091 6.952099 56.47912 104.2248 131.9934 ---------+-------------------------------------------------------------------- diff | 7.006895 13.99145 -20.94425 34.95804 ------------------------------------------------------------------------------ diff = mean(0) - mean(1) t = 0.5008 Ho: diff = 0 degrees of freedom = 64 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.6909 Pr(|T| > |t|) = 0.6182 Pr(T > t) = 0.3091 . . * 6.8 GLS random-effect model with adjustment . use source_files/dataset.dta, clear . . xtreg crying_time expectation2 age_cat group##day, i(id) re Random-effects GLS regression Number of obs = 802 Group variable: id Number of groups = 66 R-sq: Obs per group: within = 0.2879 min = 1 between = 0.4906 avg = 12.2 overall = 0.2373 max = 15 Wald chi2(31) = 324.89 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 -------------------------------------------------------------------------------------- crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---------------------+---------------------------------------------------------------- expectation2 | 11.02338 6.708016 1.64 0.100 -2.124086 24.17085 age_cat | -14.42455 7.875196 -1.83 0.067 -29.85965 1.010549 | group | Test group (TTR) | 14.05425 20.19127 0.70 0.486 -25.5199 53.62841 | day | 1 | -76.37145 14.91484 -5.12 0.000 -105.604 -47.13889 2 | -81.67646 14.76976 -5.53 0.000 -110.6247 -52.72827 3 | -89.15922 14.76976 -6.04 0.000 -118.1074 -60.21103 4 | -95.50405 14.76976 -6.47 0.000 -124.4522 -66.55586 5 | -113.8814 14.91834 -7.63 0.000 -143.1208 -84.64199 6 | -104.9159 15.07408 -6.96 0.000 -134.4606 -75.37127 7 | -118.9523 14.76976 -8.05 0.000 -147.9005 -90.00413 8 | -114.0558 14.76976 -7.72 0.000 -143.004 -85.10758 9 | -125.4881 14.91626 -8.41 0.000 -154.7235 -96.25282 10 | -115.5953 14.91626 -7.75 0.000 -144.8306 -86.35997 11 | -125.9178 14.76976 -8.53 0.000 -154.866 -96.96965 12 | -132.4254 15.0744 -8.78 0.000 -161.9706 -102.8801 13 | -136.4624 15.0744 -9.05 0.000 -166.0077 -106.9171 14 | -141.1223 14.91654 -9.46 0.000 -170.3581 -111.8864 | group#day | Test group (TTR)# 1 | -1.941118 22.25502 -0.09 0.930 -45.56015 41.67791 Test group (TTR)# 2 | -19.79086 21.55054 -0.92 0.358 -62.02914 22.44741 Test group (TTR)# 3 | -23.57646 21.68539 -1.09 0.277 -66.07904 18.92612 Test group (TTR)# 4 | 7.809209 21.69039 0.36 0.719 -34.70318 50.3216 Test group (TTR)# 5 | 11.81778 21.52714 0.55 0.583 -30.37463 54.0102 Test group (TTR)# 6 | -25.99385 21.6357 -1.20 0.230 -68.39905 16.41135 Test group (TTR)# 7 | 1.126433 21.55413 0.05 0.958 -41.11888 43.37174 Test group (TTR)# 8 | 3.829881 21.55413 0.18 0.859 -38.41543 46.07519 Test group (TTR)# 9 | -10.38317 21.52546 -0.48 0.630 -52.5723 31.80595 Test group (TTR)#10 | -27.72706 21.79213 -1.27 0.203 -70.43885 14.98473 Test group (TTR)#11 | -15.5919 21.55188 -0.72 0.469 -57.8328 26.64901 Test group (TTR)#12 | -9.563646 21.90105 -0.44 0.662 -52.48891 33.36162 Test group (TTR)#13 | .1926732 21.76232 0.01 0.993 -42.46068 42.84603 Test group (TTR)#14 | -7.948838 22.26612 -0.36 0.721 -51.58963 35.69195 | _cons | 248.2668 27.44939 9.04 0.000 194.467 302.0666 ---------------------+---------------------------------------------------------------- sigma_u | 58.285144 sigma_e | 57.105957 rho | .51021798 (fraction of variance due to u_i) -------------------------------------------------------------------------------------- . xtreg crying_time group baseline expectation2 age_cat day if(day>1), i(id) re Random-effects GLS regression Number of obs = 687 Group variable: id Number of groups = 55 R-sq: Obs per group: within = 0.0869 min = 7 between = 0.5271 avg = 12.5 overall = 0.3278 max = 13 Wald chi2(5) = 115.10 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- group | 2.040949 11.55625 0.18 0.860 -20.60889 24.69078 baseline | .3768691 .0582795 6.47 0.000 .2626434 .4910947 expectation2 | -6.276183 5.474347 -1.15 0.252 -17.00571 4.453339 age_cat | -18.15891 5.855589 -3.10 0.002 -29.63565 -6.682162 day | -4.354599 .5606468 -7.77 0.000 -5.453447 -3.255752 _cons | 121.0688 22.15384 5.46 0.000 77.64806 164.4895 -------------+---------------------------------------------------------------- sigma_u | 39.644009 sigma_e | 54.409057 rho | .34678948 (fraction of variance due to u_i) ------------------------------------------------------------------------------ . . save source_files/dataset.dta, replace file source_files/dataset.dta saved . . . . . . . * ---------------------- 7. FIGURES ------------------ . . . . * 7.1 Figure 2 Reported average crying time per time slot for each group . use Source_files/diary_full.dta, clear . merge m:m id using source_files/dataset.dta, keepusing(group) Result # of obs. ----------------------------------------- not matched 255 from master 0 (_merge==1) from using 255 (_merge==2) matched 750 (_merge==3) ----------------------------------------- . collapse h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 h16 h17 h18 h19 h20 h21 h22 h23 h24 h1 h2 h3 h4 h5 if(group==0), by(d) . outsheet d h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 h16 h17 h18 h19 h20 h21 h22 h23 h24 h1 h2 h3 h4 h5 using Data/group_control.csv, comma replace . . use Source_files/diary_full.dta, clear . merge m:m id using source_files/dataset.dta, keepusing(group) Result # of obs. ----------------------------------------- not matched 255 from master 0 (_merge==1) from using 255 (_merge==2) matched 750 (_merge==3) ----------------------------------------- . collapse h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 h16 h17 h18 h19 h20 h21 h22 h23 h24 h1 h2 h3 h4 h5 if(group==1), by(d) . outsheet d h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 h16 h17 h18 h19 h20 h21 h22 h23 h24 h1 h2 h3 h4 h5 using Data/group_test.csv, comma replace . . . . * 7.2 Figure 3 – Average daily crying time during follow-up between groups . use Source_files/dataset.dta, clear . mixed crying_time group##day || id:, var reml // Models effects Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -4348.6944 Iteration 1: log restricted-likelihood = -4348.6944 Computing standard errors: Mixed-effects REML regression Number of obs = 802 Group variable: id Number of groups = 66 Obs per group: min = 1 avg = 12.2 max = 15 Wald chi2(29) = 317.68 Log restricted-likelihood = -4348.6944 Prob > chi2 = 0.0000 -------------------------------------------------------------------------------------- crying_time | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---------------------+---------------------------------------------------------------- group | Test group (TTR) | 16.91728 20.64583 0.82 0.413 -23.5478 57.38236 | day | 1 | -75.92342 14.91191 -5.09 0.000 -105.1502 -46.6966 2 | -81.22583 14.76691 -5.50 0.000 -110.1684 -52.28322 3 | -88.70859 14.76691 -6.01 0.000 -117.6512 -59.76598 4 | -95.05342 14.76691 -6.44 0.000 -123.996 -66.1108 5 | -113.4714 14.9142 -7.61 0.000 -142.7027 -84.24012 6 | -104.6332 15.06974 -6.94 0.000 -134.1693 -75.09703 7 | -118.5017 14.76691 -8.02 0.000 -147.4443 -89.55908 8 | -113.6051 14.76691 -7.69 0.000 -142.5478 -84.66253 9 | -125.1588 14.91332 -8.39 0.000 -154.3883 -95.92917 10 | -115.2659 14.91332 -7.73 0.000 -144.4955 -86.03632 11 | -125.4672 14.76691 -8.50 0.000 -154.4098 -96.5246 12 | -131.9816 15.06981 -8.76 0.000 -161.5179 -102.4453 13 | -136.0187 15.06981 -9.03 0.000 -165.5549 -106.4824 14 | -140.6376 14.91332 -9.43 0.000 -169.8672 -111.408 | group#day | Test group (TTR)# 1 | -1.992743 22.25413 -0.09 0.929 -45.61004 41.62456 Test group (TTR)# 2 | -19.72689 21.54857 -0.92 0.360 -61.96131 22.50753 Test group (TTR)# 3 | -23.62193 21.68336 -1.09 0.276 -66.12053 18.87667 Test group (TTR)# 4 | 7.921655 21.68723 0.37 0.715 -34.58452 50.42784 Test group (TTR)# 5 | 11.94485 21.52401 0.55 0.579 -30.24144 54.13115 Test group (TTR)# 6 | -25.73955 21.63208 -1.19 0.234 -68.13764 16.65855 Test group (TTR)# 7 | 1.260278 21.55132 0.06 0.953 -40.97954 43.5001 Test group (TTR)# 8 | 3.963726 21.55132 0.18 0.854 -38.27609 46.20354 Test group (TTR)# 9 | -10.17552 21.5234 -0.47 0.636 -52.36061 32.00958 Test group (TTR)#10 | -27.54585 21.78895 -1.26 0.206 -70.2514 15.15971 Test group (TTR)#11 | -15.57829 21.55024 -0.72 0.470 -57.81597 26.6594 Test group (TTR)#12 | -9.496781 21.89635 -0.43 0.664 -52.41284 33.41928 Test group (TTR)#13 | .2131671 21.75891 0.01 0.992 -42.43351 42.85985 Test group (TTR)#14 | -7.795645 22.26144 -0.35 0.726 -51.42728 35.83599 | _cons | 234.6765 14.37591 16.32 0.000 206.5002 262.8527 -------------------------------------------------------------------------------------- ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Identity | var(_cons) | 3752.604 772.3042 2506.982 5617.126 -----------------------------+------------------------------------------------ var(Residual) | 3274.066 174.2385 2949.772 3634.013 ------------------------------------------------------------------------------ LR test vs. linear model: chibar2(01) = 399.64 Prob >= chibar2 = 0.0000 . margins group#day Adjusted predictions Number of obs = 802 Expression : Linear prediction, fixed portion, predict() ----------------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ------------------------+---------------------------------------------------------------- group#day | Control group (GTR)# 0 | 234.6765 14.37591 16.32 0.000 206.5002 262.8527 Control group (GTR)# 1 | 158.7531 15.3766 10.32 0.000 128.6155 188.8906 Control group (GTR)# 2 | 153.4506 15.23602 10.07 0.000 123.5886 183.3127 Control group (GTR)# 3 | 145.9679 15.23602 9.58 0.000 116.1058 175.8299 Control group (GTR)# 4 | 139.6231 15.23602 9.16 0.000 109.761 169.4851 Control group (GTR)# 5 | 121.205 15.37882 7.88 0.000 91.06311 151.347 Control group (GTR)# 6 | 130.0433 15.5297 8.37 0.000 99.60562 160.4809 Control group (GTR)# 7 | 116.1748 15.23602 7.63 0.000 86.31273 146.0368 Control group (GTR)# 8 | 121.0713 15.23602 7.95 0.000 91.20928 150.9334 Control group (GTR)# 9 | 109.5177 15.37797 7.12 0.000 79.37746 139.658 Control group (GTR)#10 | 119.4106 15.37797 7.77 0.000 89.27032 149.5508 Control group (GTR)#11 | 109.2093 15.23602 7.17 0.000 79.34721 139.0713 Control group (GTR)#12 | 102.6948 15.52977 6.61 0.000 72.25705 133.1326 Control group (GTR)#13 | 98.65781 15.52977 6.35 0.000 68.22002 129.0956 Control group (GTR)#14 | 94.03887 15.37797 6.12 0.000 63.89861 124.1791 Test group (TTR)# 0 | 251.5937 14.81835 16.98 0.000 222.5503 280.6372 Test group (TTR)# 1 | 173.6776 16.96572 10.24 0.000 140.4254 206.9298 Test group (TTR)# 2 | 150.641 16.16272 9.32 0.000 118.9627 182.3194 Test group (TTR)# 3 | 139.2632 16.34199 8.52 0.000 107.2335 171.2929 Test group (TTR)# 4 | 164.462 16.34712 10.06 0.000 132.4222 196.5018 Test group (TTR)# 5 | 150.0672 15.99387 9.38 0.000 118.7198 181.4146 Test group (TTR)# 6 | 121.221 15.99387 7.58 0.000 89.8736 152.5684 Test group (TTR)# 7 | 134.3523 16.16639 8.31 0.000 102.6668 166.0379 Test group (TTR)# 8 | 141.9523 16.16639 8.78 0.000 110.2668 173.6379 Test group (TTR)# 9 | 116.2595 15.99387 7.27 0.000 84.91206 147.6069 Test group (TTR)#10 | 108.782 16.34948 6.65 0.000 76.73762 140.8264 Test group (TTR)#11 | 110.5483 16.16494 6.84 0.000 78.86555 142.231 Test group (TTR)#12 | 110.1153 16.34948 6.74 0.000 78.07096 142.1597 Test group (TTR)#13 | 115.7883 16.16494 7.16 0.000 84.10555 147.471 Test group (TTR)#14 | 103.1605 16.97407 6.08 0.000 69.89193 136.4291 ----------------------------------------------------------------------------------------- . marginsplot, x(day) scheme(lean2) /// > xla(0 "D-1" 1 "D0" 2 "D1" 3 "D2" 4 "D3" 5 "D4" 6 "D5" 7 "D6" 8 "D7" 9 "D8" /// > 10 "D9" 11 "D10" 12 "D11" 13 "D12" 14 "D13", angle(45)) /// > xtitle("Days within trial") /// > yla(0 "0" 300 "300" 60(60)240, format(%02.0f) ang(h)) /// > ytitle("Daily crying time (minutes)") /// > title(Mean crying times with 95% confidence intervals) Variables that uniquely identify margins: group day . . graph export Figures/Figure3.eps, replace (file Figures/Figure3.eps written in EPS format) . . . . . . * ---------------- 8. END ---------------------------- . clear . log close name: log: /Users/paul.vaucher/OsteoPole/B-Projets/D-Research projects/R002-CUTIEs-CH/Statistic analysis/cuties.txt log type: text closed on: 19 Aug 2022, 18:37:09 ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------