## boundary (singular) fit: see help('isSingular')
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
| est | sd | cv | se | cv_mean | endpoint |
|---|---|---|---|---|---|
| 1.1505718 | 0.3165826 | 0.2751524 | 0.1827790 | 0.1588593 | albumin |
| 0.5793882 | 0.5539152 | 0.9560347 | 0.3198031 | 0.5519669 | ketone |
| 4.8093635 | 0.5041378 | 0.1048242 | 0.3564793 | 0.0741219 | il1r2 |
| 0.1416424 | 0.0038564 | 0.0272264 | 0.0027269 | 0.0192520 | glucose |
| -1.6498099 | 0.3614241 | -0.2190701 | 0.2555654 | -0.1549060 | insulin |
In this chunk, two metrics are computed across all studies for each given condition:
Simple Coefficient of Variation (CV) - i.e. no accounting for technical factors
Intra-Class Correlation Coefficient (ICC) - I used ICC2 from psych package (not ICC2k) as it seemed the most appropriate.
Note: data were not log-transformed.
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
| condition | endpoint | ICC | CV |
|---|---|---|---|
| High HCT-HG | albumin | 0.5532544 | 0.3991602 |
| High HCT-HG | ketone | 0.5658850 | 0.3049145 |
| High HCT-HG | il1r2 | 0.9235520 | 0.2457077 |
| High HCT-HG | insulin | 0.3841673 | 0.8958944 |
| High HCT-HG | glucose | 0.7534388 | 0.0482676 |
| High HCT-NG | albumin | 0.5763549 | 0.2572888 |
| High HCT-NG | ketone | 0.0030842 | 0.4971109 |
| High HCT-NG | il1r2 | 0.0501867 | 0.4131444 |
| High HCT-NG | insulin | 0.3857564 | 1.5858973 |
| High HCT-NG | glucose | 0.4457201 | 0.1167323 |
| Low HCT-HG | albumin | 0.1402081 | 0.5324163 |
| Low HCT-HG | ketone | 0.2091522 | 0.2342297 |
| Low HCT-HG | il1r2 | 0.7427219 | 0.5331425 |
| Low HCT-HG | insulin | 0.5004977 | 0.7510045 |
| Low HCT-HG | glucose | 0.8757985 | 0.0627144 |
| Low HCT-NG | albumin | 0.2772667 | 0.3460743 |
| Low HCT-NG | ketone | 0.0000000 | 0.3206010 |
| Low HCT-NG | il1r2 | 0.7392075 | 0.5451335 |
| Low HCT-NG | insulin | 0.4985921 | 0.9275471 |
| Low HCT-NG | glucose | 0.8607399 | 0.0707763 |
Here, we find the ICC and CV, but calculate it per-study and per-condition. I used ICC2 from psych package (not ICC2k) as it seemed the most appropriate.
Note: data were not log-transformed.
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
| condition | endpoint | study | ICC | CV |
|---|---|---|---|---|
| High HCT-HG | albumin | Study 1 | 0.5806151 | 0.3654457 |
| High HCT-HG | albumin | Study 2 | 0.4794971 | 0.3705305 |
| High HCT-HG | albumin | Study 3 | 0.8900719 | 0.2067851 |
| High HCT-HG | ketone | Study 1 | 0.8434458 | 0.1630355 |
| High HCT-HG | ketone | Study 2 | 0.4589309 | 0.3818775 |
| High HCT-HG | ketone | Study 3 | 0.2938115 | 0.3800412 |
| High HCT-HG | il1r2 | Study 2 | 0.9389784 | 0.1450800 |
| High HCT-HG | il1r2 | Study 3 | 0.9772931 | 0.0733322 |
| High HCT-HG | insulin | Study 1 | 0.9053309 | 0.5063891 |
| High HCT-HG | insulin | Study 2 | 0.9461248 | 0.4809310 |
| High HCT-HG | glucose | Study 1 | 0.7244151 | 0.0346589 |
| High HCT-HG | glucose | Study 2 | 0.8819179 | 0.0513563 |
| High HCT-NG | albumin | Study 1 | 0.5345070 | 0.2782865 |
| High HCT-NG | albumin | Study 2 | 0.7913464 | 0.2378688 |
| High HCT-NG | albumin | Study 3 | 0.6400615 | 0.3379235 |
| High HCT-NG | ketone | Study 1 | 0.6181710 | 0.2052161 |
| High HCT-NG | ketone | Study 2 | 0.3935942 | 0.1884120 |
| High HCT-NG | ketone | Study 3 | 0.8358665 | 0.3039191 |
| High HCT-NG | il1r2 | Study 2 | 0.0000000 | 0.2595635 |
| High HCT-NG | il1r2 | Study 3 | 0.3579468 | 0.2225435 |
| High HCT-NG | insulin | Study 1 | 0.9406554 | 0.1880961 |
| High HCT-NG | insulin | Study 2 | 0.8130398 | 1.5858973 |
| High HCT-NG | glucose | Study 1 | 0.9649046 | 0.0240061 |
| High HCT-NG | glucose | Study 2 | 0.6443449 | 0.0635424 |
| Low HCT-HG | albumin | Study 1 | 0.4997950 | 0.5885357 |
| Low HCT-HG | albumin | Study 2 | 0.4514274 | 0.2486935 |
| Low HCT-HG | albumin | Study 3 | 0.0675256 | 0.3445829 |
| Low HCT-HG | ketone | Study 1 | 0.0930372 | 0.1366018 |
| Low HCT-HG | ketone | Study 2 | 0.0970900 | 0.1859814 |
| Low HCT-HG | ketone | Study 3 | 0.9355984 | 0.0762750 |
| Low HCT-HG | il1r2 | Study 2 | 0.9870501 | 0.3957020 |
| Low HCT-HG | il1r2 | Study 3 | 0.9799020 | 0.2463113 |
| Low HCT-HG | insulin | Study 1 | 0.9866203 | 0.2293909 |
| Low HCT-HG | insulin | Study 2 | 0.8584641 | 0.4050092 |
| Low HCT-HG | glucose | Study 1 | 0.9275354 | 0.0540907 |
| Low HCT-HG | glucose | Study 2 | 0.9266640 | 0.0343019 |
| Low HCT-NG | albumin | Study 1 | 0.8050722 | 0.2518356 |
| Low HCT-NG | albumin | Study 2 | 0.7433340 | 0.1708972 |
| Low HCT-NG | albumin | Study 3 | 0.3490273 | 0.2626604 |
| Low HCT-NG | ketone | Study 1 | 0.4219087 | 0.0761203 |
| Low HCT-NG | ketone | Study 2 | 0.4636688 | 0.1321250 |
| Low HCT-NG | ketone | Study 3 | 0.3546682 | 0.1540906 |
| Low HCT-NG | il1r2 | Study 2 | 0.9687175 | 0.4633230 |
| Low HCT-NG | il1r2 | Study 3 | 0.6647262 | 0.4986884 |
| Low HCT-NG | insulin | Study 1 | 0.9534604 | 0.1995606 |
| Low HCT-NG | insulin | Study 2 | 0.9274458 | 0.6163073 |
| Low HCT-NG | glucose | Study 1 | 0.9077396 | 0.0935303 |
| Low HCT-NG | glucose | Study 2 | 0.9601137 | 0.0287145 |
Analyses were conducted using the R Statistical language (version 4.3.1; R Core Team, 2023) on Windows 10 x64 (build 19045), using the packages magrittr (version 2.0.3; Bache S, Wickham H, 2022), lme4 (version 1.1.35.3; Bates D et al., 2015), Matrix (version 1.5.4.1; Bates D et al., 2023), lubridate (version 1.9.3; Grolemund G, Wickham H, 2011), plotrix (version 3.8.4; J L, 2006), lmerTest (version 3.1.3; Kuznetsova A et al., 2017), emmeans (version 1.10.1; Lenth R, 2024), report (version 0.5.8; Makowski D et al., 2023), tibble (version 3.2.1; Müller K, Wickham H, 2023), ggplot2 (version 3.5.1; Wickham H, 2016), forcats (version 1.0.0; Wickham H, 2023), stringr (version 1.5.1; Wickham H, 2023), tidyverse (version 2.0.0; Wickham H et al., 2019), readxl (version 1.4.3; Wickham H, Bryan J, 2023), dplyr (version 1.1.4; Wickham H et al., 2023), purrr (version 1.0.2; Wickham H, Henry L, 2023), readr (version 2.1.5; Wickham H et al., 2024), tidyr (version 1.3.1; Wickham H et al., 2024) and psych (version 2.4.3; William Revelle, 2024).