ASYMPTOTIC INFORMATION IN CENSORED SURVIVAL DATA
成果类型：
Article
署名作者：
OAKES, D
署名单位：
Harvard University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/64.3.441
发表日期：
1977
页码：
441448
关键词：
摘要：
Various procedures are considered for fitting a regression model to censored survival data in continuous time with time-dependent covariate functions. These include maximum likelihood with the underlying hazard function known completely and known up to a multiplicative constant and the maximization of Cox''s partial likelihood. Explicit formulae for the asymptotic variances of the estimators are derived informally and compared. It is shown how sample second derivatives may be used to estimate the amount of information lost through lack of knowledge of the underlying hazard function. Corresponding results for a more general parameterization, which includes the Weibull hazard function, are indicated.