Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes
This work addresses the need for better subgroup identification in clinical studies with censored outcomes, which is important for personalized medicine, though it appears incremental as it builds on existing mixture and regularization techniques.
The authors tackled the problem of identifying sparse, interpretable subgroups with heterogeneous treatment effects in time-to-event data subject to censoring, and demonstrated their method's efficacy in recovering such phenotypes in real-world clinical studies on cardiovascular health.
Studies involving both randomized experiments as well as observational data typically involve time-to-event outcomes such as time-to-failure, death or onset of an adverse condition. Such outcomes are typically subject to censoring due to loss of follow-up and established statistical practice involves comparing treatment efficacy in terms of hazard ratios between the treated and control groups. In this paper we propose a statistical approach to recovering sparse phenogroups (or subtypes) that demonstrate differential treatment effects as compared to the study population. Our approach involves modelling the data as a mixture while enforcing parameter shrinkage through structured sparsity regularization. We propose a novel inference procedure for the proposed model and demonstrate its efficacy in recovering sparse phenotypes across large landmark real world clinical studies in cardiovascular health.