  Precision medicine is an emerging field that takes into account individual
heterogeneity to inform better clinical practice. In clinical trials, the
evaluation of treatment effect heterogeneity is an important component, and
recently, many statistical methods have been proposed for stratifying patients
into different subgroups based on such heterogeneity. However, the majority of
existing methods developed for this purpose focus on the case with a
dichotomous treatment and are not directly applicable to multi-arm trials. In
this paper, we consider the problem of patient stratification in multi-arm
trial settings and propose a two-stage procedure within the Bayesian
nonparametric framework. Specifically, we first use Bayesian additive
regression trees (BART) to predict potential outcomes (treatment responses)
under different treatment options for each patient, and then we leverage
Bayesian profile regression to cluster patients into subgroups according to
their baseline characteristics and predicted potential outcomes. We further
embed a variable selection procedure into our proposed framework to identify
the patient characteristics that actively "drive" the clustering structure. We
conduct simulation studies to examine the performance of our proposed method
and demonstrate the method by applying it to a UK-based multi-arm blood
donation trial, wherein our method uncovers five clinically meaningful donor
subgroups.
