INFERENCE FOR EVENTS WITH DEPENDENT RISKS IN MULTIPLE END-POINT STUDIES

• 成果类型: Article
• 署名作者: PEPE, MS
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290411
• 发表日期: 1991
• 页码: 770-778
• 关键词: product-limit estimator; censored survival-data; versus-host disease; counting-processes; competing risks; large sample; tests; graft

摘要

Kaplan-Meier and cumulative incidence functions are not sufficient descriptive devices for studies that have multiple time-to-event endpoints. For example, in cancer treatment research the probability of tumor recurrence conditional on not having died from treatment-related toxicities and the prevalence of graft-versus-host disease among leukemia-free patients surviving a bone marrow transplant are of interest. These quantities can be estimated nonparametrically using simple functions of several Kaplan-Meier and cumulative incidence estimates for events with possibly dependent risks. We derive asymptotic distribution theory for such functions by representing Kaplan-Meier, cumulative incidence, and cumulative hazard estimators as sums of iid random variables. Variance estimation also follows directly from this representation. Two-sample test statistics with asymptotic null distribution theory are presented. Several examples illustrate the utility of these results.