Department of Population and Quantitative Health Sciences
Statistics and Probability
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.
Meta-analysis, odds-ratio, random probabilities, random sample sizes, random-effects model
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DOI of Published Version
Kulinskaya E, Hoaglin DC, Bakbergenuly I. Exploring consequences of simulation design for apparent performance of methods of meta-analysis. Stat Methods Med Res. 2021 Jul;30(7):1667-1690. doi: 10.1177/09622802211013065. Epub 2021 Jun 10. PMID: 34110941; PMCID: PMC8411476. Link to article on publisher's site
Statistical methods in medical research
Kulinskaya E, Hoaglin DC, Bakbergenuly I. (2021). Exploring consequences of simulation design for apparent performance of methods of meta-analysis. Population and Quantitative Health Sciences Publications. https://doi.org/10.1177/09622802211013065. Retrieved from https://escholarship.umassmed.edu/qhs_pp/1454
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.