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Abstract:
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Interval-censored data arise when the event of interest can only be ascertained through periodic examinations. In medical studies, subjects may not complete the examination schedule for reasons related to the event of interest. In this paper, we develop a semiparametric approach to adjust for such informative dropout in the regression analysis of interval-censored data. Specifically, we propose a broad class of joint models, under which the event or failure time of interest follows a transformation model with a random effect and the dropout time follows a different transformation model but with the same random effect. We consider nonparametric maximum likelihood estimation for the joint models and develop an EM algorithm that involves simple and stable calculations. We establish that the resulting estimators of the regression parameters are consistent, asymptotic normal, and asymptotically efficient. In addition, we assess the performance of the proposed numerical and inferential procedures through extensive simulation studies. Finally, we provide an application to data on the incidence of diabetes derived from a major epidemiological cohort study.
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