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Lianming Wang, University of South Carolina
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| Title: Nonhomogeneous Poisson models for panel count data and interval-censored failure time data |
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Abstract: In many epidemiological and medical studies, subjects are examined at regular or irregular follow-up visits. Panel count data arise when the response of interest is the count of some repeated events between consecutive examination times, while interval-censored data arise when the response of interest is the time to some particular event and only the status of the event is known at each examination time. Poisson process has been popular to model the panel count data in the literature. In this talk, we propose a gamma-frailty nonhomogeneous Poisson process model for analyzing panel count data to account for the within-subject correlation and develop an easy estimation method using EM algorithm. We also propose a computationally efficient method for analyzing general interval-censored data under the PH model using an EM algorithm. We developed a novel data augmentation by introducing a latent nonhomogeneous Poisson process to expand the observed likelihood. Both approaches have shown excellent performance in terms of estimation accuracy and computational advantages, such as being robust to initial values, converging fast, and providing variance estimates in closed-form. A joint modeling of panel count responses and the interval-censored failure time of a terminal event is discussed as a generalization of the proposed approaches.
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