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

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

Full text not available from this repository. (Request a copy)

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 standardized mean difference (SMD), we provide the results from extensive simulations on 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; the new method (KDB) based on the improved approximation to the distribution of the Q statistic by Kulinskaya, Dollinger and Bj{\o}rkest{\o}l (2011) ), five interval estimators for $\tau^2$ (profile likelihood, Q-profile, Biggerstaff and Jackson, Jackson, and the new KDB 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).

Item Type:	Article
Additional Information:	20 pages and full simulation results, comprising 130 figures, each presenting 12 combinations of sample sizes and numbers of studies
Uncontrolled Keywords:	stat.me
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and Statistics
Depositing User:	LivePure Connector
Date Deposited:	11 Jun 2020 01:28
Last Modified:	09 Dec 2021 10:02
URI:	https://ueaeprints.uea.ac.uk/id/eprint/75541
DOI:

Actions (login required)

View Item