Studying treatment-effect heterogeneity in precision medicine through induced subgroups

Precision medicine, in the sense of tailoring the choice of medical treatment to patients’ pretreatment characteristics, is nowadays gaining a lot of attention. Preferably, this tailoring should be realized in an evidencebased way, with key evidence in this regard pertaining to subgroups of patients that respond differentially to treatment (i.e., to subgroups involved in treatment–subgroup interactions). Often a-priori hypotheses on subgroups involved in treatment–subgroup interactions are lacking or are incomplete at best. Therefore, methods are needed that can induce such subgroups from empirical data on treatment effectiveness in a post hoc manner. Recently, quite a few such methods have been developed. So far, however, there is little empirical experience in their usage. This may be problematic for medical statisticians and statistically minded medical researchers, as many (nontrivial) choices have to be made during the dataanalytic process. The main purpose of this paper is to discuss the major concepts and considerations when using these methods. This discussion will be based on a systematic, conceptual, and technical analysis of the type of research questions at play, and of the type of data that the methods canhandle along with the available software, and a review of available empirical evidence. We will illustrate all this with the analysis of a dataset comparing several anti-depressant treatments.