Exploring Consequences of Simulation Design for Apparent Performance of Methods of Meta-analysis

Kulinskaya, Elena, Hoaglin, David C. and Bakbergenuly, Ilyas (2021) Exploring Consequences of Simulation Design for Apparent Performance of Methods of Meta-analysis. Statistical Methods in Medical Research. ISSN 0962-2802

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Abstract

Contemporary statistical publications rely on simulation to evaluate performance of new methods and compare them with established methods. In the context of random-effects meta-analysis of log-odds-ratios, we investigate how choices in generating data affect such conclusions. The choices we study include the overall log-odds-ratio, the distribution of probabilities in the control arm, and the distribution of study-level sample sizes. We retain the customary normal distribution of study-level effects. To examine the impact of the components of simulations, we assess the performance of the best available inverse–variance–weighted two-stage method, a two-stage method with constant sample-size-based weights, and two generalized linear mixed models. The results show no important differences between fixed and random sample sizes. In contrast, we found differences among data-generation models in estimation of heterogeneity variance and overall log-odds-ratio. This sensitivity to design poses challenges for use of simulation in choosing methods of meta-analysis.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 28 May 2021 00:34
Last Modified: 21 Jul 2021 01:47
URI: https://ueaeprints.uea.ac.uk/id/eprint/80152
DOI: 10.1177/09622802211013065

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