Censoring Robust Estimation of Covariate Effects on Discrete Survival Endpoints

描述（由申请人提供）：离散生存终点可能发生在定期随访或离散测量时间的观察性研究中。这些数据通常出现的例子包括通过例行检查和监测进行亚临床疾病诊断的情况。这些包括系统使用结肠镜检查来检测结直肠癌中的息肉生长，以及在乳腺癌的情况下使用常规乳房X线摄影来检测肿瘤肿块。在这样的设置中，确切的事件时间是区间删失的并且不可观察的。当生存终点被认为是离散或区间删失时，用于分析观察到的失效时间的常见半参数方法包括离散时间比例风险模型和比例优势模型。当半参数假设不成立时，使用半参数生存模型的推断依赖于观察到的删失分布。这使得结果的解释在科学上没有意义，因为目标人群定义不清。最近，一些作者提出了使用加权估计，以消除删失的影响时，半参数假设不成立的估计。虽然这些建议的估计提供了一致的和可重复的结果下模型的误设定，他们只开发了生存时间的连续测量的设置，只有在K样本比较的情况下。本文的研究目的是建立一类截尾稳健离散生存估计，并证明其渐近性态。此外，我们将扩展以前的工作截尾连续生存数据的稳健估计，开发这些方法的一般回归策略，可能涉及多个调整协变量。 公共卫生相关性：本文的研究目的是建立一类截尾稳健离散生存估计，并证明其渐近性态。此外，我们将扩展以前的工作截尾连续生存数据的稳健估计，开发这些方法的一般回归策略，可能涉及多个调整协变量。

DESCRIPTION (provided by applicant): Discrete survival endpoints can occur in observational studies where there is periodic follow-up or when time is measured discretely. Examples where these data commonly arise include situations where subclinical diagnosis of disease is made through routine checkups and surveillance. These include the systematic use of colonoscopy for detecting polyp growth in colorectal cancer and the use of regular mammographies for the detection of tumor mass in the case of breast cancer. In such settings, the exact event times are interval censored and unobserved. When the survival endpoint is acknowledged to be discrete or interval censored, common semiparametric methods for the analysis of the observed failure times include the discrete-time proportional hazards model and the proportional odds model. Inference using semiparametric survival models is dependent on the observed censoring distribution when the semiparametric assumption fails to hold. This renders the interpretation of the results scientifically unmeaningful since the target population i ill-defined. Recently, some authors have proposed the use of weighted estimators to remove the effect of censoring on the estimand of interest when semiparametric assumptions fail to hold. While these proposed estimators provide consistent and reproducible results under model misspecification, they have only been developed for settings where survival times are measured continuously and only in the case of K-sample comparisons. The goal of the research proposed here is to develop a class of censoring robust discrete survival estimators and establish their asymptotic behavior. In addition, we will extend the previous work censoring robust estimators for continuous survival data by developing these approaches for general regression strategies that may involve multiple adjustment covariates. PUBLIC HEALTH RELEVANCE: The goal of the research proposed here is to develop a class of censoring robust discrete survival estimators and establish their asymptotic behavior. In addition, we will extend the previous work censoring robust estimators for continuous survival data by developing these approaches for general regression strategies that may involve multiple adjustment covariates.