A Bayesian subgroup analysis with a zero‐enriched Polya Urn scheme
Siva Sivaganesan, Purushottam W. Laud, Peter Müller · Statistics in Medicine · 2010
We introduce a new approach to inference for subgroups in clinical trials. We use Bayesian model selection, and a threshold on posterior model probabilities to identify subgroup effects for reporting. For each covariate of interest, we define a separate class of models, and use the posterior probability associated with each model and the threshold to determine the existence of a subgroup effect. As usual in Bayesian clinical trial design we compute frequentist operating characteristics, and achieve the desired error probabilities by choosing an appropriate threshold(s) for the posterior probabilities.