R code
In the current study, the CCC, AIC and DeLong test were calculated by R and the corresponding packages. 
1. For CCC calculation:
library(DescTools)
AB42 <- read.table("AB42.csv", sep=',', header=TRUE)
AB40 <- read.table("AB42.csv", sep=',', header=TRUE)
Ttau <- read.table("Ttau.csv", sep=',', header=TRUE)
Ptau <- read.table("Ptau.csv", sep=',', header=TRUE)
nfl <- read.table("nfl.csv", sep=',', header=TRUE)
AB42.ccc <- CCC(AB42[,1], AB42[,2], ci = "z-transform", conf.level = 0.95)
AB40.ccc <- CCC(AB40[,1], AB40[,2], ci = "z-transform", conf.level = 0.95)
Ttau.ccc <- CCC(Ttau[,1], Ttau[,2], ci = "z-transform", conf.level = 0.95)
Ptau.ccc <- CCC(Ptau[,1], Ptau[,2], ci = "z-transform", conf.level = 0.95)
nfl .ccc <- CCC(nfl [,1], nfl [,2], ci = "z-transform", conf.level = 0.95)
print(AB42.ccc) 
print(AB40.ccc) 
print(Ttau.ccc) 
print(Ptau.ccc) 
print(nfl.ccc) 
2. For AIC calculation:
library(ggplot2)
library(pROC)
df_baseline <- read.table("df_baseline.csv", sep=',', header=TRUE)
df_followup <- read.table("df_followup.csv", sep=',', header=TRUE)
df_cohort2 <- read.table("df_chhort2.csv", sep=',', header=TRUE)
model1 <- glm(diagnosis ~ P_ab42, data=df_baseline, family = 'binomial')
model2 <- glm(diagnosis ~ P_ptau , data=df_baseline, family = 'binomial')
model3 <- glm(diagnosis ~ P_nfl, data=df_baseline, family = 'binomial')
model4 <- glm(diagnosis ~ P_ab42 + P_ptau + P_nfl, data=df_baseline, family = 'binomial')
model5 <- glm(diagnosis ~ P_ab42 + P_ptau + P_nfl +APOE, data=df_baseline, family = 'binomial')
model6 <- glm(diagnosis ~ P_ab42, data=df_followup, family = 'binomial')
model7 <- glm(diagnosis ~ P_ptau, data=df_followup, family = 'binomial')
model8 <- glm(diagnosis ~ P_nfl, data=df_followup, family = 'binomial')
model9 <- glm(diagnosis ~ P_ab42 + P_ptau + P_nfl, data=df_followup, family = 'binomial')
model10 <- glm(diagnosis ~ P_ab42 + P_ptau + P_nfl + APOE, data=df_followup, family = 'binomial')
model11 <- glm(diagnosis ~ P_ab42, data=df_cohort2, family = 'binomial')
model12 <- glm(diagnosis ~ P_ptau, data=df_cohort2, family = 'binomial')
model13 <- glm(diagnosis ~ P_nfl, data=df_cohort2, family = 'binomial')
model14 <- glm(diagnosis ~ P_ab42 + P_ptau + P_nfl, data=df_cohort2, family = 'binomial')
AIC1 <- AIC(model1)
AIC2 <- AIC(model2)
AIC3 <- AIC(model3)
AIC4 <- AIC(model4)
AIC5 <- AIC(model5)
AIC6 <- AIC(model6)
AIC7 <- AIC(model7)
AIC8 <- AIC(model8)
AIC9 <- AIC(model9)
AIC10 <- AIC(model10)
AIC11 <- AIC(model11)
AIC12 <- AIC(model12)
AIC13 <- AIC(model13)
AIC14 <- AIC(model14)
3.  For DeLong test:
p1 <- predict(model1)
p2 <- predict(model2)
p3 <- predict(model3)
p4 <- predict(model4)
p5 <- predict(model5)
p6 <- predict(model6)
p7 <- predict(model7)
p8 <- predict(model8)
p9 <- predict(model9)
p10 <- predict(model10)
p11 <- predict(model11)
p12 <- predict(model12)
p13 <- predict(model13)
p14 <- predict(model14)
roc1 <- roc(df_baseline$diagnosis,p1, levels = c('0','1'))
roc2 <- roc(df_baseline$diagnosis,p2, levels = c('0','1'))
roc3 <- roc(df_baseline$diagnosis,p3, levels = c('0','1'))
roc4 <- roc(df_baseline$diagnosis,p4, levels = c('0','1'))
roc5 <- roc(df_baseline$diagnosis,p5, levels = c('0','1'))
roc6 <- roc(df_baseline$diagnosis,p6, levels = c('0','1'))
roc7 <- roc(df_baseline$diagnosis,p7, levels = c('0','1'))
roc8 <- roc(df_baseline$diagnosis,p8, levels = c('0','1'))
roc9 <- roc(df_baseline$diagnosis,p9, levels = c('0','1'))
roc10 <- roc(df_baseline$diagnosis,p10, levels = c('0','1'))
roc11 <- roc(df_cohort2$diagnosis,p11, levels = c('0','1'))
roc12 <- roc(df_cohort2$diagnosis,p12, levels = c('0','1'))
roc13 <- roc(df_cohort2$diagnosis,p13, levels = c('0','1'))
roc14 <- roc(df_cohort2$diagnosis,p14, levels = c('0','1'))
P_value1 <- roc.test(roc1, roc5,method = 'delong')
P_value2 <- roc.test(roc2, roc5,method = 'delong')
P_value3 <- roc.test(roc3, roc5,method = 'delong')
P_value4 <- roc.test(roc4, roc5,method = 'delong')
P_value5 <- roc.test(roc6, roc10,method = 'delong')
P_value6 <- roc.test(roc7, roc10,method = 'delong')
P_value7 <- roc.test(roc8, roc10,method = 'delong')
P_value8 <- roc.test(roc9, roc10,method = 'delong')
P_value9 <- roc.test(roc11, roc14,method = 'delong')
P_value10 <- roc.test(roc12, roc14,method = 'delong')
P_value11 <- roc.test(roc13, roc14,method = 'delong')