Simulation study of estimating between-study variance and overall effect in meta-analyses of mean difference

Bakbergenuly, Ilyas, Hoaglin, David C. and Kulinskaya, Elena (2019) Simulation study of estimating between-study variance and overall effect in meta-analyses of mean difference. ArXiv e-prints.

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Abstract

Methods for random-effects meta-analysis require an estimate of the between-study variance, $\tau^2$. The performance of estimators of $\tau^2$ (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 $\tau^2$ (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 \cite{kulinskaya2004welch}), five interval estimators for $\tau^2$ (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 $\tau^2$ 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 $\tau^2$, 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.

Item Type: Article
Additional Information: 20 pages and 108 A4 format 4 by 3 display figures on simulation results. arXiv admin note: substantial text overlap with arXiv:1903.01362
Uncontrolled Keywords: stat.me
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 11 Jun 2020 01:28
Last Modified: 11 Jun 2020 01:28
URI: https://ueaeprints.uea.ac.uk/id/eprint/75542
DOI:

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