文章基本信息

标题：An Approximate Likelihood Procedure for Competing Risks Data
本地全文：下载
作者：Akio Suzukawa
期刊名称：JOURNAL OF THE JAPAN STATISTICAL SOCIETY
印刷版ISSN：1882-2754
电子版ISSN：1348-6365
出版年度：2010
卷号：40
期号：2
页码：239-263
DOI：10.14490/jjss.40.239
出版社：JAPAN STATISTICAL SOCIETY
摘要：Parametric estimation of cause-specific hazard functions in a competing risks model is considered. An approximate likelihood procedure for estimating parameters of cause-specific hazard functions based on competing risks data subject to right censoring is proposed. In an assumed parametric model that may have been misspecified, an estimator of a parameter is said to be consistent if it converges in probability to the pseudo-true value of the parameter as the sample size becomes large. Under censorship, the ordinary maximum likelihood method does not necessarily give consistent estimators. The proposed approximate likelihood procedure is consistent even if the parametric model is misspecified. An asymptotic distribution of the approximate maximum likelihood estimator is obtained, and the efficiency of the estimator is discussed. Datasets from a simulation experiment, an electrical appliance test, and a pneumatic tire test are used to illustrate the procedure.
关键词：Aalen-Johansen estimator;cause-specific cumulative incidence function;cause-specific hazard function;censored data;Kaplan-Meier estimator