Online Program Home
My Program

Abstract Details

Activity Number: 27 - SPEED: Survival Analysis
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #330784
Title: Semiparametric Regression Analysis of Length-Biased Interval-Censored Data
Author(s): Fei Gao* and Kwun Chuen Gary Chan
Companies: University of Washington and University of Washington
Keywords: Left truncation; Nonparametric maximum likelihood estimation; Proportional hazards model; Semiparametric efficiency

In prevalent cohort design, subjects who have experienced an initial event but not the failure event are preferentially enrolled. The prospective follow-up can be subject to interval censoring, where the failure time is not recorded precisely but is rather known to lie within a time interval. When the incidence of the initial event is stationary over time, a prevalent cohort collects length-biased data. Statistical methodology for regression modeling of length-biased data are mostly proposed for uncensored and right-censored data. In this paper, we study the nonparametric maximum likelihood estimation for the proportional hazards model with length-biased interval-censored data. We develop a simple and computational stable EM algorithm through introducing pseudo truncated data and latent Poisson random variables. We establish the strong consistency and asymptotic normality of the proposed estimators and provide the inference through the profile likelihood approach. We assess the performance of the proposed numerical and inferential procedures through extensive simulation studies and apply the proposed methods to the data derived from the Massachusetts Health Care Panel Study.

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

Back to the full JSM 2018 program