Maximum likelihood estimation for semiparametric transformation models with interval-censored data

作     者：Zeng, Donglin Mao, Lu Lin, D. Y. 

作者机构：Univ N Carolina Dept Biostat Chapel Hill NC 27599 USA 

出 版 物：《BIOMETRIKA》 (生物测量学)

年 卷 期：2016年第103卷第2期

页      面：253-271页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：U.S. National Institutes of Health 

主　　题：Current-status data EM algorithm Interval censoring Linear transformation model Nonparametric likelihood Proportional hazards Proportional odds Semiparametric efficiency Time-dependent covariate 

摘      要：Interval censoring arises frequently in clinical, epidemiological, financial and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasses proportional hazards and proportional odds models. We consider nonparametric maximum likelihood estimation for this class of models with an arbitrary number of monitoring times for each subject. We devise an EM-type algorithm that converges stably, even in the presence of time-dependent covariates, and show that the estimators for the regression parameters are consistent, asymptotically normal, and asymptotically efficient with an easily estimated covariance matrix. Finally, we demonstrate the performance of our procedures through simulation studies and application to an HIV/AIDS study conducted in Thailand.