Simulation study of estimating between-study variance and overall effect in meta-analyses of mean difference
share
Methods for random-effects meta-analysis require an estimate of the between-study variance, τ^2. The performance of estimators of τ^2 (measured by bias and coverage) affects their usefulness in assessing heterogeneity of study-level effects, and also the performance of related estimators of the overall effect. For the effect measure mean difference (MD), we review five point estimators of τ^2 (the popular methods of DerSimonian-Laird, restricted maximum likelihood, and Mandel and Paule (MP); the less-familiar method of Jackson; and a new method (WT) based on the improved approximation to the distribution of the Q statistic by kulinskaya2004welch), five interval estimators for τ^2 (profile likelihood, Q-profile, Biggerstaff and Jackson, Jackson, and the new WT method), six point estimators of the overall effect (the five related to the point estimators of τ^2 and an estimator whose weights use only study-level sample sizes), and eight interval estimators for the overall effect (five based on the point estimators for τ^2, the Hartung-Knapp-Sidik-Jonkman (HKSJ) interval, a modification of HKSJ, and an interval based on the sample-size-weighted estimator). We obtain empirical evidence from extensive simulations and an example.
READ FULL TEXT