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. Causal 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.
翻译:对现实世界临床干预措施治疗效率的估计涉及与可能受到审查的连续结果,如时间到死亡、再住院、或综合事件等持续结果一起工作,在这种假设情景中,因果关系推理要求将影响基准存活率的混杂生理特征的影响与所评估干预措施的影响脱钩。在本文件中,我们提出了一个潜在的变数方法,通过建议个人可以属于具有不同反应特征的潜在群体之一来模拟不同治疗效果。我们表明,这种潜在结构可以调节基本生存率,并有助于确定干预的效果。我们展示了我们基于个人在最初为评估适当治疗以减少心血管风险而进行的多次大规模随机临床试验中的治疗反应,根据他们的治疗反应发现可操作型个人的能力。