## Drawing the wrong conclusion about subgroups: a comparison of Bayes and frequentist methods

In the previous post, I simulated data from a hypothetical RCT that had heterogeneous treatment effects across subgroups defined by three covariates. I presented two Bayesian models, a strongly pooled model and an unpooled version, that could be used to estimate all the subgroup effects in a single model. I compared the estimates to a set of linear regression models that were estimated for each subgroup separately.
My goal in doing these comparisons is to see how often we might draw the wrong conclusion about subgroup effects when we conduct these types of analyses.
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