Exploring consequences of simulation design for apparent performance of methods of meta-analysis

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 in...

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Bibliographic Details
Published in:Statistical methods in medical research 2021-07, Vol.30 (7), p.1667-1690
Main Authors: Kulinskaya, Elena, Hoaglin, David C., Bakbergenuly, Ilyas
Format: Article
Language:English
Subjects:
Computer Simulation
Heterogeneity
Linear Models
Meta-analysis
Models, Statistical
Normal distribution
Odds Ratio
Performance evaluation
Sample Size
Simulation
Statistical analysis
Statistical methods
Statistical models
Variance
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Summary: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.
ISSN:0962-2802
1477-0334
DOI:10.1177/09622802211013065