Methods of diagnostic meta-analysis: comparing the generalized linear mixed model and the split component synthesis model

Abstract

Purpose of review Diagnostic meta-analyses combine data from several diagnostic test accuracy (DTA) studies to provide an in-depth assessment of a specific diagnostic test's performance across diverse populations and settings. Additionally, knowledge on common methods of diagnostic meta-analyses is crucial for researchers to make informed decisions on best practice for reporting analyses and results. This article provides an overview of commonly used methods of diagnostic meta-analyses using real-life and simulation data. Recent findings Advances in methods of diagnostic meta-analyses in recent years have increased uncertainty among researchers in relation to the most suitable method to be used. Currently, the most popular approaches for diagnostic evidence synthesis include hierarchical summary operating characteristic (HSROC) and bivariate random effects models though other methods such as the split component synthesis method have been proposed. In addition, different software modules exist for DTA meta-analyses. Summary This article presents a thorough evaluation of current frequentist DTA meta-analysis methods implementing both simulated and real-world data. By understanding the recent methods of diagnostic meta-analyses and their limitations, clinicians may better be equipped in selecting the optimum approach to improve clinical judgement and consequently better patient outcomes.