10.6084/m9.figshare.7757018.v1
Aniek Sies
Aniek
Sies
Koen Demyttenaere
Koen
Demyttenaere
Iven Van Mechelen
Iven
Van Mechelen
Studying treatment-effect heterogeneity in precision medicine through induced subgroups
Taylor & Francis Group
2019
Precision medicine
treatment-effect heterogeneity
exploratory subgroup analysis
treatment–subgroup interactions
clinical trials
2019-02-22 14:47:12
Journal contribution
https://tandf.figshare.com/articles/journal_contribution/Studying_treatment-effect_heterogeneity_in_precision_medicine_through_induced_subgroups/7757018
<p>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 evidence-based 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 <i>post hoc</i> 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 data-analytic 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 can handle 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.</p>