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Erschienen in:
2022 | OriginalPaper | Buchkapitel
Maximum Likelihood Estimation of Semiparametric Regression Models with Interval-Censored Data
verfasst von : D. Y. Lin, Donglin Zeng
Erschienen in: Emerging Topics in Modeling Interval-Censored Survival Data
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Abstract
Interval censoring arises frequently in clinical, epidemiological, financial, and sociological studies, where the event or failure of interest is not observed at an exact time point but is rather known to occur within a time interval induced by periodic examinations. We formulate the effects of potentially time-dependent covariates on the failure time through the semiparametric Cox proportional hazards model. We study nonparametric maximum likelihood estimation with an arbitrary number of examination times for each study subject. We present an EM algorithm that involves very simple calculations and converges stably for any dataset, even in the presence of time-dependent covariates. The resulting estimators for the regression parameters are consistent, asymptotically normal, and asymptotically efficient with an easily estimated covariance matrix. In addition, we extend the EM algorithm and asymptotic theory to competing risks and multivariate failure time data. Finally, we provide applications to real medical studies.