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.

翻译：涉及随机实验和观测数据的研究通常包含时间至事件结局，例如故障时间、死亡或不良状况的发生。这类结局通常因随访丢失而受到删失，既定的统计实践涉及通过处理组与对照组之间的风险比来比较治疗效果。本文提出一种统计方法，用于恢复与总体研究人群相比显示出差异性治疗效应的稀疏表型组（或亚型）。我们的方法将数据建模为混合模型，同时通过结构化稀疏正则化实现参数收缩。我们为所提模型提出了一种新颖的推断程序，并证明其在恢复心血管健康领域大型里程碑式临床研究中的稀疏表型方面的有效性。