Maximum likelihood estimation in semiparametric regression models with censored data

• 成果类型: Article
• 署名作者: Zeng, D.; Lin, D. Y.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1369-7412.2007.00606.x
• 发表日期: 2007
• 页码: 507-536
• 关键词: PROPORTIONAL HAZARDS MODEL; failure time data; transformation models; information bounds; ASYMPTOTIC THEORY; longitudinal data; survival-data; inference; Consistency; ratio

摘要

Semiparametric regression models play a central role in formulating the effects of covariates on potentially censored failure times and in the joint modelling of incomplete repeated measures and failure times in longitudinal studies. The presence of infinite dimensional parameters poses considerable theoretical and computational challenges in the statistical analysis of such models. We present several classes of semiparametric regression models, which extend the existing models in important directions. We construct appropriate likelihood functions involving both finite dimensional and infinite dimensional parameters. The maximum likelihood estimators are consistent and asymptotically normal with efficient variances. We develop simple and stable numerical techniques to implement the corresponding inference procedures. Extensive simulation experiments demonstrate that the inferential and computational methods proposed perform well in practical settings. Applications to three medical studies yield important new insights. We conclude that there is no reason, theoretical or numerical, not to use maximum likelihood estimation for semiparametric regression models. We discuss several areas that need further research.