Joint Inference for Competing Risks Survival Data

• 成果类型: Article
• 署名作者: Li, Gang; Yang, Qing
• 署名单位: University of California System; University of California Los Angeles; Duke University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1093942
• 发表日期: 2016
• 页码: 1289-1300
• 关键词: PROPORTIONAL HAZARDS MODEL; Goodness-of-fit; cumulative incidence functions; nonparametric-tests; REGRESSION-MODEL; subdistribution hazards; cox regression; censored-data; rates; EQUALITY

摘要

This article develops joint inferential methods for,the cause-specific hazard function and the cumulative incidence function of a specific type of failure to assess the effects of a variable on the time to the type of failure of interest in the presence of competing risks. Joint inference for the two functions are needed in practice because (i) they describe different characteristics of a given type of failure, (ii) they do not uniquely determine each other, and (iii) the effects of a variable on the two functions can be different and one often does not know which effects are to be expected. We study both the group comparison problem and the regression problem. We also discuss joint inference for other related functions. Our simulation shows that our joint tests can be considerably more powerful than the Bonferroni method; which has important practical implications to the analysis and design of clinical studies, with competing risks data. We illustrate our method using a Hodgkin disease data and a lymphoma data. Supplementary materials for this article are available online.