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A Class of Semiparametric Transformation Models for Doubly Censored Failure Time Data
Author(s) -
Li Shuwei,
Hu Tao,
Wang Peijie,
Sun Jianguo
Publication year - 2018
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12319
Subject(s) - estimator , mathematics , inference , accelerated failure time model , semiparametric regression , expectation–maximization algorithm , econometrics , semiparametric model , estimating equations , poisson distribution , censoring (clinical trials) , statistical inference , parametric statistics , statistics , class (philosophy) , maximum likelihood , proportional hazards model , computer science , artificial intelligence
Doubly censored failure time data occur in many areas including demographical studies, epidemiology studies, medical studies and tumorigenicity experiments, and correspondingly some inference procedures have been developed in the literature (Biometrika, 91, 2004, 277; Comput. Statist. Data Anal., 57, 2013, 41; J. Comput. Graph. Statist., 13, 2004, 123). In this paper, we discuss regression analysis of such data under a class of flexible semiparametric transformation models, which includes some commonly used models for doubly censored data as special cases. For inference, the non‐parametric maximum likelihood estimation will be developed and in particular, we will present a novel expectation–maximization algorithm with the use of subject‐specific independent Poisson variables. In addition, the asymptotic properties of the proposed estimators are established and an extensive simulation study suggests that the proposed methodology works well for practical situations. The method is applied to an AIDS study.