Censoring Robust Estimation of Covariate Effects on Discrete Survival Endpoints

描述(由申请人提供)：离散生存终点可出现在有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常规检查和监测对疾病进行亚临床诊断的情况。这些措施包括系统地使用结肠镜检查来检测结直肠癌息肉的生长，以及在乳腺癌的情况下使用定期乳房X光检查来检测肿块。在这样的设置中，准确的事件时间是间隔审查的，并且不被观察到。当生存终点被确认为离散或区间截尾时，用于分析观察到的失效时间的常用半参数方法包括离散时间比例风险模型和比例赔率模型。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截尾分布。这使得对结果的解释在科学上没有意义，因为目标人群定义不明确。最近，一些作者提出，当半参数假设不成立时，使用加权估计来消除截尾对估计和感兴趣的影响。虽然这些建议的估计器在模型错误指定的情况下提供了一致和可重现的结果，但它们只在生存时间被连续测量且仅在K样本比较的情况下被开发。本文提出的研究目标是发展一类截尾稳健离散生存估计量，并建立其渐近行为。此外，我们将扩展以前的工作，审查连续生存数据的稳健估计，为可能涉及多个调整协变量的一般回归策略开发这些方法。

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.