Many 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 prognoses. Realistically, measured covariates can rarely capture all such associations with certainty. For such dependent censoring, often covariate measurements are at best proxies of underlying prognoses. In this paper, we establish a nonparametric identification framework by formally admitting that conditional independent censoring may fail in practice and accounting for 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 illustrate our framework with concrete settings, where we examine the finite-sample performance of our proposed estimators via a Monte-Carlo simulation and apply them to the SEER-Medicare dataset.

翻译：许多流行病学和临床研究旨在分析时间至事件终点。一个常见问题是右删失。在某些情况下，右删失是因为受试者在研究结束后仍存活或迁出研究区域，此时右删失通常被视为独立或非信息性删失。通过利用可能随时间变化的协变量信息（如有），可进一步放宽该假设为条件独立删失，即在协变量分层内假设删失与失效时间独立。然而在其他情形下，事件可能被其他竞争事件（如死亡）所删失，且可能通过预后因素与删失相关联。现实中，所测协变量很少能完全捕捉此类关联。对于此类相依删失，协变量测量通常只是潜在预后的近似替代。本文通过正式承认条件独立删失在实践中可能失效，并将协变量测量视为潜在关联的不完美替代，建立了一个非参数识别框架。该框架提出自适应估计量，并在通用假设下证明其具有一致性、渐近正态性和双重稳健性。我们通过具体场景展示该框架，并利用蒙特卡洛模拟考察所提估计量的有限样本性能，最后将其应用于SEER-Medicare数据集。