A hierarchical regression approach to meta‐analysis of diagnostic test accuracy evaluations

TLDR

Meta‑analytic models for diagnostic test accuracy must account for both within‑ and between‑study variability, yet current approaches largely rely on fixed‑effects frameworks. This paper proposes a hierarchical regression model to meta‑analyse studies reporting test sensitivity and specificity. The model permits varying test stringency and accuracy across studies, incorporates study‑specific covariates, and estimates parameters via Markov Chain Monte Carlo simulation in BUGS, offering flexible summary statistics. Applying the model to a recent cervical‑cancer nodal‑metastasis test comparison demonstrates its advantages over fixed‑effects methods. © 2001 John Wiley & Sons, Ltd.

Abstract

Abstract An important quality of meta‐analytic models for research synthesis is their ability to account for both within‐ and between‐study variability. Currently available meta‐analytic approaches for studies of diagnostic test accuracy work primarily within a fixed‐effects framework. In this paper we describe a hierarchical regression model for meta‐analysis of studies reporting estimates of test sensitivity and specificity. The model allows more between‐ and within‐study variability than fixed‐effect approaches, by allowing both test stringency and test accuracy to vary across studies. It is also possible to examine the effects of study specific covariates. Estimates are computed using Markov Chain Monte Carlo simulation with publicly available software (BUGS). This estimation method allows flexibility in the choice of summary statistics. We demonstrate the advantages of this modelling approach using a recently published meta‐analysis comparing three tests used to detect nodal metastasis of cervical cancer. Copyright © 2001 John Wiley & Sons, Ltd.