### INTRODUCTION

### UNDERSTANDING DIAGNOSTIC TEST ACCURACY

### Summary statistics for diagnostic test accuracy

### Diagnostic test accuracy model

### Calculation of effect size

### DIAGNOSTIC TEST ACCURACY USING THE “mada” AND “meta” PACKAGES OF R

### Data coding and loading

### Summary statistics

### Univariate analysis

#### Sensitivity

^{2}of the heterogeneity is determined by subtracting the number of degrees of freedom from the Cochrane Q statistics, and then again dividing the resulting value by the Cochrane Q statistics. Thus, it quantifies the heterogeneity in a consistent manner. Values between 0% and 40% indicate that the heterogeneity may not be important; values between 30% and 60% indicate moderate heterogeneity; values between 50% and 90% indicate substantial heterogeneity; and values between 75% and 100% indicate considerable heterogeneity. The p-value of the Cochrane Q statistics is 0.1, which is a somewhat wide range of significance [3].

^{2}is 32.5% and the Cochrane Q statistics p-value is 0.158, which suggest weak heterogeneity.

■ Forest plot

· forest(sensitivity_logit, digits=3, rightcols=c(“effect”, “ci”), xlab=“Sensitivity”)

#### Specificity

· specificity_logit <- metaprop(dta_shim$TN, dta_shim$TN+ dta_shim$FP, comb.fixed=FALSE, comb.random=TRUE, sm=“PLOGIT”, method.ci=“CP”, studlab=dta_shim$id, byvar=dta_shim$g)

·print(specificity_logit, digits=3)

^{2}in this specificity analysis is 78.3%, and the p-value of Cochrane Q statistics is <0.0001, which indicates the existence of heterogeneity.

■ Forest plot

· forest(specificity_logit, digits=3, rightcols=c(“effect”, “ci”), xlab=“Specificity”)

#### Diagnostic odds ratio

■ Forest plot

· forest(DOR_model, digits=3, rightcols=c(“effect”, “ci”), xlab =“Diagnostic Odds Ratio”)

^{2}of all studies is 72.7%, and the p-value of the Cochrane Q statistics is 0.0003, indicating that there is heterogeneity.

### Bivariate analysis

#### Diagnostic test accuracy summary line (summary receiver operating characteristic curve)

### Heterogeneity review

■ Sensitivity and specificity correlation coefficient

Finally, to examine the correlation coefficient of sensitivity and specificity, additional variables are created for the current data as follows:

· dta_shim$sn <- dta_shim$TP/(dta_shim$TP+dta_shim$FN)

·dta_shim$sp <- dta_shim$TN/(dta_shim$FP+dta_shim$TN)

·dta_shim$logitsn <- log(dta_shim$sn/(1-dta_shim$sn))

·dta_shim$logitsp <- log(dta_shim$sp/(1-dta_shim$sp))

■ Meta regression analysis

The “mada” package does not provide functions for the meta regression analysis of the DTA. Therefore, the statistical significance of the moderating variable subgroup (Western European countries vs. other countries) is verified by performing meta regression analysis with the DOR as the effect size.

·library(meta)

·metareg(DOR_model, g, method.tau=“REML,” digits=3)