Subgroup analysis involves the comparison of treatment efficacies in a clinical trial among subgroups defined by baseline patient characteristics, such as gender and age. Better subgroup analysis techniques can improve the extraction of information from clinical trials and lead to improved clinical trial designs. This chapter presents methods that cast subgroup analysis as a model selection procedure that can also be seen as a decision problem. Bayesian thinking allows one readily to formulate the probability model and to implement inference and decision making. The material presented here can be seen as a framework in which to carry out subgroup analysis rather than a specific instance of it.
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