# Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes

## TL;DR

This paper introduces a new statistical method for identifying sparse, interpretable subgroups with different treatment effects in censored time-to-event data, enhancing personalized treatment analysis in clinical studies.

## Contribution

It proposes a mixture model with structured sparsity regularization and a novel inference procedure for recovering phenogroups with differential effects in censored survival data.

## Key findings

- Successfully recovers sparse phenotypes in cardiovascular studies
- Demonstrates effectiveness on large real-world clinical datasets
- Improves understanding of subgroup-specific treatment effects

## Abstract

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.