Methods for estimating between‐study variance and overall effect in meta‐analysis of odds ratios. (13th April 2020)

• Record Type: Journal Article
• Authors: Bakbergenuly, Ilyas; Hoaglin, David C.; Kulinskaya, Elena
• Is Part Of: Research synthesis methods. Volume 11:Number 3(2020)
• Page Start: 426
• Page End: 442
• Publication Date: 2020-04-13
• Subjects: between‐study variance -- binary outcome -- heterogeneity -- meta‐analysis -- random‐effects model; Research -- Methodology -- Periodicals; Research -- Statistical methods -- Periodicals; 507.2
• DOI: 10.1002/jrsm.1404
• Languages: English
• ISSNs: 1759-2879

Abstract

Abstract : In random‐effects meta‐analysis the between‐study variance ( τ 2 ) has a key role in assessing heterogeneity of study‐level estimates and combining them to estimate an overall effect. For odds ratios the most common methods suffer from bias in estimating τ 2 and the overall effect and produce confidence intervals with below‐nominal coverage. An improved approximation to the moments of Cochran's Q statistic, suggested by Kulinskaya and Dollinger (KD), yields new point and interval estimators of τ 2 and of the overall log‐odds‐ratio. Another, simpler approach (SSW) uses weights based only on study‐level sample sizes to estimate the overall effect. In extensive simulations we compare our proposed estimators with established point and interval estimators for τ 2 and point and interval estimators for the overall log‐odds‐ratio (including the Hartung‐Knapp‐Sidik‐Jonkman interval). Additional simulations included three estimators based on generalized linear mixed models and the Mantel‐Haenszel fixed‐effect estimator. Results of our simulations show that no single point estimator of τ 2 can be recommended exclusively, but Mandel‐Paule and KD provide better choices for small and large numbers of studies, respectively. The KD estimator provides reliable coverage of τ 2 . Inverse‐variance‐weighted estimators of the overall effect are substantially biased, as are the Mantel‐Haenszel odds ratio and the estimators from the generalized linear mixed models. The SSW estimator of the overall effect and a related confidence interval provide reliable point and interval estimation of the overall log‐odds‐ratio.