Methods for estimating between-study variance and overall effect in meta-analysis of odds ratios.

Ilyas Bakbergenuly, David C Hoaglin, Elena Kulinskaya

Research synthesis methods 2020 May

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In random-effects meta-analysis the between-study variance ( τ2 ) 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 τ2 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 τ2 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 τ2 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 τ2 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 τ2 . 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. © 2020 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

Citation

Ilyas Bakbergenuly, David C Hoaglin, Elena Kulinskaya. Methods for estimating between-study variance and overall effect in meta-analysis of odds ratios. Research synthesis methods. 2020 May;11(3):426-442