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Semiparametric regression analysis of length‐biased interval‐censored data
Author(s) -
Gao Fei,
Chan Kwun Chuen Gary
Publication year - 2019
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12970
Subject(s) - censoring (clinical trials) , statistics , estimator , proportional hazards model , nonparametric statistics , mathematics , asymptotic distribution , expectation–maximization algorithm , econometrics , consistency (knowledge bases) , maximization , censored regression model , likelihood function , estimating equations , maximum likelihood , mathematical optimization , geometry
In prevalent cohort design, subjects who have experienced an initial event but not the failure event are preferentially enrolled and the observed failure times are often length‐biased. Moreover, the prospective follow‐up may not be continuously monitored and failure times are subject to interval censoring. We study the nonparametric maximum likelihood estimation for the proportional hazards model with length‐biased interval‐censored data. Direct maximization of likelihood function is intractable, thus we develop a computationally simple and stable expectation‐maximization algorithm through introducing two layers of data augmentation. We establish the strong consistency, asymptotic normality and efficiency of the proposed estimator and provide an inferential procedure through profile likelihood. We assess the performance of the proposed methods through extensive simulations and apply the proposed methods to the Massachusetts Health Care Panel Study.