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

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-ce...

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Bibliographic Details
Published inBiometrika Vol. 103; no. 2; p. 253
Main Authors Zeng, Donglin, Mao, Lu, Lin, D Y
Format Journal Article
LanguageEnglish
Published England 01.06.2016
Subjects
Interval censoring
Current-status data
Proportional odds
Nonparametric likelihood
Time-dependent covariate
Linear transformation model
Semiparametric efficiency
Proportional hazards
EM algorithm
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Summary: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.
ISSN:0006-3444
DOI:10.1093/biomet/asw013