# "Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies"
#  by Hisashi Noma, Yuki Matsushima and Ryota Ishii
#  Communications in Statistics: Case Studies and Data Analysis 2021; 7, 344-358.

#  R example code for calculating the 95% confidence interval of the AUC of SROC curve

# The "dmetatools" package
# CRAN page: https://cran.r-project.org/web/packages/dmetatools/

###

# Install and load the "dmetatools" package

install.packages("dmetatools")

library(dmetatools)
library(mada)


# Load the example dataset

data(cervical)

CT <- cervical[cervical$method==1,]
LAG <- cervical[cervical$method==2,]
MRI <- cervical[cervical$method==3,]


# DTA meta-analysis using the Reitsma model
fit1 <- reitsma(CT)
summary(fit1)
fit2 <- reitsma(LAG)
summary(fit2)
fit3 <- reitsma(MRI)
summary(fit3)


# Making the SROC curves
plot(fit1)
lines(sroc(fit2), lty=2, col="blue")
ROCellipse(fit2, lty=2, pch=2, add=TRUE, col="blue")
lines(sroc(fit3), lty=3, col="red")
ROCellipse(fit3, lty=3, pch=3, add=TRUE, col="red")
points(fpr(CT), sens(CT), cex = .5)
points(fpr(LAG), sens(LAG), pch = 2, cex = 0.5, col="blue")
points(fpr(MRI), sens(MRI), pch = 3, cex = 0.5, col="red")
legend("bottomright", c("CT", "LAG", "MRI"), pch = 1:3, lty = 1:3, col=c("black","blue","red"))


# Computation of the 95% CI for the AUC of SROC curve

AUC_boot(CT$TP,CT$FP,CT$FN,CT$TN)
AUC_boot(LAG$TP,LAG$FP,LAG$FN,LAG$TN)
AUC_boot(MRI$TP,MRI$FP,MRI$FN,MRI$TN)


# Comparing the differences of AUCs

AUC_comparison(CT$TP,CT$FP,CT$FN,CT$TN,LAG$TP,LAG$FP,LAG$FN,LAG$TN)
AUC_comparison(MRI$TP,MRI$FP,MRI$FN,MRI$TN,LAG$TP,LAG$FP,LAG$FN,LAG$TN)
AUC_comparison(MRI$TP,MRI$FP,MRI$FN,MRI$TN,CT$TP,CT$FP,CT$FN,CT$TN)