Censoring Robust Estimation of Covariate Effects on Discrete Survival Endpoints

审查离散生存终点协变量效应的鲁棒估计

• 批准号: 8245596
• 负责人: Daniel L Gillen
• 金额: $ 7.12万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8245596
• 关键词: Behavior; Clinical; Clinical Trials; Colonoscopy; Colorectal Adenoma; Colorectal Cancer; Controlled Study; DL-alpha-Difluoromethylornithine; Data; Detection; Event; Exhibits; Failure; Goals; Growth; Health Sciences; Literature; Mammography; Measures; Methodology; Methods; Modeling; Observational Study; Pattern; Performance; Phase; Polyps; Population; Prevention; Procedures; Property; Proportional Hazards Models; Relative (related person); Research; Sampling; Sulindac; Target Populations; Time; Weight; Work; censorship; checkup examination; disease diagnosis; follow-up; interest; malignant breast neoplasm; neglect; simulation; time interval; tumor; user friendly software

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.

描述（由申请人提供）：在定期随访或离散测量时间的观察性研究中可能会出现离散的生存终点。这些数据通常出现的例子包括通过常规检查和监测进行疾病亚临床诊断的情况。其中包括系统地使用结肠镜检查来检测结直肠癌中的息肉生长，以及使用定期乳房X光检查来检测乳腺癌中的肿瘤块。在这种情况下，确切的事件时间是经过间隔审查且无法观察到的。当生存终点被认为是离散或区间删失时，用于分析观察到的故障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理取决于观察到的审查分布。这使得对结果的科学解释毫无意义，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，使用加权估计量来消除审查对感兴趣估计量的影响。虽然这些提出的估计量在模型错误指定的情况下提供了一致且可重复的结果，但它们仅针对连续测量生存时间的设置而开发，并且仅在 K 样本比较的情况下进行。这里提出的研究的目标是开发一类审查鲁棒离散生存估计器并建立它们的渐近行为。此外，我们将通过开发这些可能涉及多个调整协变量的一般回归策略的方法来扩展之前审查连续生存数据的稳健估计量的工作。 公共卫生相关性：此处提出的研究目标是开发一类审查稳健离散生存估计量并建立其渐近行为。此外，我们将通过开发这些可能涉及多个调整协变量的一般回归策略的方法来扩展之前审查连续生存数据的稳健估计量的工作。