Estimation of treatment efficacy of real-world clinical interventions
involves working with continuous outcomes such as time-to-death,
re-hospitalization, or a composite event that may be subject to censoring.
Counterfactual reasoning in such scenarios requires decoupling the effects of
confounding physiological characteristics that affect baseline survival rates
from the effects of the interventions being assessed. In this paper, we present
a latent variable approach to model heterogeneous treatment effects by
proposing that an individual can belong to one of latent clusters with distinct
response characteristics. We show that this latent structure can mediate the
base survival rates and helps determine the effects of an intervention. We
demonstrate the ability of our approach to discover actionable phenotypes of
individuals based on their treatment response on multiple large randomized
clinical trials originally conducted to assess appropriate treatments to reduce
cardiovascular risk.

本文提出了一种潜变量方法，用于模拟异质性治疗效果，并演示其在多个大型随机对照试验中发现个体可操作表型的能力。