Tutorial: statistical methods for the meta-analysis of diagnostic test accuracy studies

Abstract

This tutorial shows how to perform a meta-analysis of diagnostic test accuracy studies (DTA) based on a 2 × 2 table available for each included primary study. First, univariate methods for meta-analysis of sensitivity and specificity are presented. Then the use of univariate logistic regression models with and without random effects for e.g. sensitivity is described. Diagnostic odds ratios (DOR) are then introduced to combine sensitivity and specificity into one single measure and to assess publication bias. Finally, bivariate random effects models using the exact binomial likelihood to describe within-study variability and a normal distribution to describe between-study variability are presented as the method of choice. Based on this model summary receiver operating characteristic (sROC) curves are constructed using a regression model logit-true positive rate (TPR) over logit-false positive rate (FPR). Also it is demonstrated how to perform the necessary calculations with the freely available software R. As an example a meta-analysis of DTA studies using Procalcitonin as a diagnostic marker for sepsis is presented.

Keywords: Procalcitonin; area under the curve (AUC); diagnostic test accuracy (DTA); generalized linear mixed model (GLMM); meta-analysis; sensitivity; specificity; summary operator curve (sROC).