Modern epidemiological and clinical studies aim at analyzing a time-to-event endpoint. A common complication is right censoring. In some cases, it arises because subjects are still surviving after the study terminates or move out of the study area, in which case right censoring is typically treated as independent or non-informative. Such an assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate information, if available, assuming censoring and failure time are independent among covariate strata. In yet other instances, events may be censored by other competing events like death and are associated with censoring possibly through prognosis. Realistically, measured covariates can rarely capture all such association with certainty. For such dependent censoring, often covariate measurements are at best proxies of underlying prognosis. In this paper, we establish a nonparametric identification framework by formally accounting for the covariate measurements as imperfect proxies of underlying association. The framework suggests adaptive estimators which we give generic assumptions under which they are consistent, asymptotically normal and doubly robust. We consider a concrete setting to illustrate our framework, where we examine the finite-sample performance of our proposed estimators via extensive simulations.
翻译:现代流行病学和临床研究旨在分析时间到活动终点。 常见的复杂情况是正确地审查。 在某些情况下,这是因为研究结束后或离开研究领域后,学生仍然健在,通常将右审查视为独立或非信息规范。 这种假设可以进一步放松为有条件的独立审查,办法是利用可能存在的时间变化的共变信息(如果有的话),假设检查和失败时间在共变阶段之间是独立的。在另一些情况下,事件可能受到诸如死亡等其他相互竞争的事件的审查,并可能与审查有关,可能通过预测进行。现实的、测量的共变情况很少能肯定地捕捉所有这种关联。对于这种依赖性审查,通常会相互差异的测量是基础预测的最佳替代物。在本文中,我们通过正式将共变的测量结果算为基础联系的不完善的预兆。这个框架建议了我们给出的通用假设,这些假设是一致的,我们用正常的和双重的模型来进行。 我们认为,我们通过一个具体的模拟来具体地展示我们提出的弹性框架。