A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring

• 成果类型: Article
• 署名作者: Othus, Megan; Li, Yi; Tiwari, Ram C.
• 署名单位: Harvard University; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; US Food & Drug Administration (FDA); National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.tm08033
• 发表日期: 2009
• 页码: 1241-1250
• 关键词: sufficient follow-up; prostate-cancer; transformation models; cell-survival; FAILURE; regression; Identifiability; probability; cholesterol; disease

摘要

Modern cancer treatments have substantially improved cure rates and have generated a great interest in and need for proper statistical tools to analyze survival data with nonnegligible cure fractions. Data with Cure fractions often ire complicated by dependent censoring, and the analysis of this type of data typically involves untestable parametric assumptions on the dependence of the censoring mechanism and the true survival times. Motivated by the analysis of prostate cancer survival trends, we propose a class of semiparametric transformation cure models that allows for dependent censoring without making parametric assumptions on the dependence relationship. The proposed class of models encompasses a number of common models for the latency survival function, including the proportional hazards model and the proportional odds model, and also allows for time-dependent covariates. An inverse censoring probability reweighting scheme is used to derive unbiased estimating equations. Small-sample properties with simulations are derived, and the method is demonstrated with a data application.