ArXiv Preprint
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.
Chirag Nagpal, Vedant Sanil, Artur Dubrawski
2023-02-24
