Abstract：
We propose a novel methodology to quantify the effect of stochastic interventions for a non-terminal intermediate time-to-event on a terminal time-to-event outcome. Investigating these effects is particularly important in health disparities research when we seek to quantify inequities in the timely delivery of treatment and its impact on patients\' survival time. Current approaches fail to account for time-to-event intermediates and semi-competing risks arising in this setting. Under the potential outcome framework, we define causal contrasts relevant in health disparities research and provide identifiability conditions when stochastic interventions on an intermediate non-terminal time-to-event are of interest. Causal contrasts are estimated in continuous time within a multistate modeling framework and analytic formulae for the estimators of the causal contrasts are developed. We show via simulations that ignoring censoring in intermediate and/or terminal time-to-event processes or ignoring semi-competing risks may give misleading results. This work demonstrates that a rigorous definition of the causal effects and joint estimation of the terminal outcome and intermediate non-terminal time-to-event distributions are crucial for valid investigation of interventions and mechanisms in continuous time. We employ this novel methodology to investigate the role of delaying treatment uptake in explaining racial disparities in cancer survival in a cohort study of colon cancer patients.
摘要：
我们提出了一种新颖的方法来量化非最终中间时间到事件的随机干预对最终时间到事件结果的影响。当我们寻求量化及时提供治疗的不平等及其对患者生存时间的影响时，调查这些影响在健康差异研究中尤为重要。当前的方法未能考虑到在这种情况下出现的时间到事件的中间体和半竞争风险。在潜在结果框架下，我们定义了与健康差异研究相关的因果对比,并在对中间非终点事件发生时间进行随机干预时提供了可识别性条件.在多状态建模框架内连续估计因果对比，并开发了因果对比估计器的解析公式。我们通过模拟表明，忽略中间和/或最终事件时间过程中的审查或忽略半竞争风险可能会产生误导性结果。这项工作表明，对因果效应的严格定义以及对最终结果和中间非最终时间到事件分布的联合估计对于连续时间内有效调查干预措施和机制至关重要。我们使用这种新颖的方法来研究延迟治疗摄取在解释结肠癌患者队列研究中癌症生存的种族差异中的作用。
