  Personalized medicine aims at identifying best treatments for a patient with
given characteristics. It has been shown in the literature that these methods
can lead to great improvements in medicine compared to traditional methods
prescribing the same treatment to all patients. Subgroup identification is a
branch of personalized medicine which aims at finding subgroups of the patients
with similar characteristics for which some of the investigated treatments have
a better effect than the other treatments. A number of approaches based on
decision trees has been proposed to identify such subgroups, but most of them
focus on the two-arm trials (control/treatment) while a few methods consider
quantitative treatments (defined by the dose). However, no subgroup
identification method exists that can predict the best treatments in a scenario
with a categorical set of treatments. We propose a novel method for subgroup
identification in categorical treatment scenarios. This method outputs a
decision tree showing the probabilities of a given treatment being the best for
a given group of patients as well as labels showing the possible best
treatments. The method is implemented in an R package \textbf{psica} available
at CRAN. In addition to numerical simulations based on artificial data, we
present an analysis of a community-based nutrition intervention trial that
justifies the validity of our method.
