Performance of statistical methods for meta-analysis when true study effects are non-normally distributed: A simulation study
Top Cited Papers
- 9 December 2010
- journal article
- research article
- Published by SAGE Publications in Statistical Methods in Medical Research
- Vol. 21 (4), 409-426
- https://doi.org/10.1177/0962280210392008
Abstract
Meta-analysis (MA) is a statistical methodology that combines the results of several independent studies considered by the analyst to be ‘combinable’. The simplest approach, the fixed-effects (FE) model, assumes the true effect to be the same in all studies, while the random-effects (RE) family of models allows the true effect to vary across studies. However, all methods are only correct asymptotically, while some RE models assume that the true effects are normally distributed. In practice, MA methods are frequently applied when study numbers are small and the normality of the effect distribution unknown or unlikely. In this article, we discuss the performance of the FE approach and seven frequentist RE MA methods: DerSimonian–Laird, Q-based, maximum likelihood, profile likelihood, Biggerstaff–Tweedie, Sidik–Jonkman and Follmann–Proschan. We covered numerous scenarios by varying the MA sizes (small to moderate), the degree of heterogeneity (zero to very large) and the distribution of the effect sizes (normal, skew-normal and ‘extremely’ non-normal). Performance was evaluated in terms of coverage (Type I error), power (Type II error) and overall effect estimation (accuracy of point estimates and error intervals).Keywords
This publication has 32 references indexed in Scilit:
- Quantifying heterogeneity in a meta-analysisStatistics in Medicine, 2002
- Investigating causes of heterogeneity in systematic reviewsStatistics in Medicine, 2002
- Approaches to heterogeneity in meta‐analysisStatistics in Medicine, 2001
- Methods for Exploring Heterogeneity in Meta-AnalysisEvaluation & the Health Professions, 2001
- A comparison of statistical methods for meta‐analysisStatistics in Medicine, 2001
- Evaluation of Old and New Tests of Heterogeneity in Epidemiologic Meta-AnalysisAmerican Journal of Epidemiology, 1999
- Detecting and describing heterogeneity in meta-analysisStatistics in Medicine, 1998
- Meta-analysis: Potentials and promiseBMJ, 1997
- Systematic Review: Why sources of heterogeneity in meta-analysis should be investigatedBMJ, 1994
- Primary, Secondary, and Meta-Analysis of ResearchEducational Researcher, 1976