Unsupervised clustering approach to assess heterogeneity of treatment effects across patient phenotypes in randomized clinical trials

BackgroundPrimary results from randomized clinical trials (RCT) only inform on the average treatment effect in the studied population, and it is critical to understand how treatment effect varies across subpopulations. In this paper we describe a clustering-based approach for the assessment of Heterogeneity of Treatment Effect (HTE) over patient phenotypes, which maintains the unsupervised nature of classical subgroup analysis while jointly accounting for relevant patient characteristics.