Simulations for estimation of heterogeneity variance ${\tau }^2$ in constant and inverse variance weights meta-analysis of log-odds-ratios

Abstract

A number of popular estimators of the between-study variance, ${\tau }^2$, are based on the Cochran's $Q$ statistic for testing heterogeneity in meta analysis. We introduce new point and interval estimators of ${\tau }^2$ for log-odds-ratio. These include new DerSimonian-Kacker-type moment estimators based on the first moment of $Q_F$, the $Q$ statistic with effective-sample-size weights, and novel median-unbiased estimators. We study, by simulation, bias and coverage of these new estimators of ${\tau }^2$ and, for comparative purposes, bias and coverage of a number of well-known estimators based on the $Q$ statistic with inverse-variance weights, $Q_{IV}$, such as the Mandel-Paule, DerSimonian-Laird, and restricted-maximum-likelihood estimators, and an estimator based on the Kulinskaya-Dollinger (2015) improved approximation to $Q_{IV}$.

180 figures, 199 pages in total