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Data‐generating models of dichotomous outcomes: Heterogeneity in simulation studies for a random‐effects meta‐analysis
Author(s) -
Pateras Konstantinos,
Nikolakopoulos Stavros,
Roes Kit
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7569
Subject(s) - random effects model , meta analysis , econometrics , computer science , statistics , study heterogeneity , mathematics , medicine
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 3 alternative data‐generating models, with larger differences apparent in the small population setting. Our findings are relevant to multilevel binomial models in general.