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BACKGROUND: When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment. METHODS: Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy. RESULTS: Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis. CONCLUSIONS: The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis.

Original publication




Journal article


BMC Med Res Methodol

Publication Date





Evidence synthesis, Individual patient data, Interaction effects, Meta-analysis, Random-effects, Subgroup analysis, Computational Biology, Data Interpretation, Statistical, Evidence-Based Medicine, Humans, Meta-Analysis as Topic, Models, Statistical, Randomized Controlled Trials as Topic