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Activity Number: 6 - Recent Advance of Nonparametric and Semiparametric Techniques with Complex Data Structure
Type: Invited
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #325456 Presentation
Title: Semiparametric Regression Analysis of Multiple Right- and Interval-Censored Events
Author(s): Fei Gao and Donglin Zeng* and Danyu Lin
Companies: University of Washington and UNC Chapel Hill and University of North Carolina
Keywords: Interval censored; Right censored; Cox model; Risk prediction; NPMLE

Health sciences research often involves both right- and interval-censored events because the occurrence of a symptomatic disease can only be observed up to the end of follow-up while the occurrence of an asymptomatic disease can only be detected through periodic examinations. We formulate the effects of potentially time dependent covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects that capture the dependence both within and between the two types of events. We consider nonparametric maximum likelihood estimation and develop a simple and stable EM algorithm for computation. We show that the resulting estimators are consistent and the parametric components are asymptotically normal and efficient with a covariance matrix that can be consistently estimated by profile likelihood or nonparametric bootstrap. In addition, we provide dynamic prediction of disease incidence based on the evolving event history. Furthermore, we assess the performance of the proposed methods through extensive simulation studies. Finally, we provide an application to a major epidemiological cohort study.

Authors who are presenting talks have a * after their name.

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