Dataset for: Data generating models of dichotomous outcomes: Heterogeneity in simulation studies for a random-effects meta-analysis

Simulation studies to evaluate performance of statistical methods require a well specified Data Generating Model. Details of these models are essential to interpret the results and arrive at proper conclusions. A case in point is random-effects meta-analysis of dichotomous outcomes. We reviewed a number of simulation studies that evaluated approximate normal models for meta-analysis of dichotomous outcomes and we assessed the data generating models that were used to generate events for a series of (heterogeneous) trials. We demonstrate that the performance of the statistical methods, as assessed by simulation, differs between these three alternative Data Generating Models, with larger differences apparent in the small population setting. Our findings are relevant to multilevel binomial models in general.

评估统计方法性能的模拟研究，需采用设定严谨的数据生成模型（Data Generating Model）。此类模型的具体细节，对于解读研究结果、推导合理结论至关重要。典型案例之一便是二分类结局的随机效应元分析。我们梳理了多项针对二分类结局元分析的近似正态模型的模拟研究，并针对一系列（异质性）试验中用于生成结局事件的生成模型开展了评估。我们的研究表明，经模拟评估的统计方法性能，在这三种备选数据生成模型之间存在显著差异，且在小总体情境下差异尤为突出。本研究结论总体上适用于多层二项分布模型。