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Activity Number: 544
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #314799
Title: Conditional Modeling of Longitudinal Data with Terminal Event
Author(s): Shengchun Kong* and Bin Nan and John D. Kalbfleisch
Companies: Purdue University and University of Michigan and University of Michigan
Keywords: nonlinear mixed effect model ; Cox regression model ; Empirical process ; Pseudo-maximum likelihood estimation
Abstract:

We consider longitudinal data analysis with a terminal event where the terminal event time is informative. Existing methods include the joint modeling approach and the marginal estimating equation approach, and both assume that the relationship between the response variable and a set of covariates is the same no matter whether the terminal event occurs or not. This assumption, however, is not reasonable for many longitudinal studies. Therefore we directly model event time as a covariate, which provides intuitive interpretation. When the terminal event times are right-censored, a semiparametric likelihood-based approach is proposed for the parameter estimation, where the Cox regression model is used for the censored terminal event time. We consider a two-stage estimation procedure, where the conditional distribution of the right-censored terminal event time given other variables is estimated prior to maximizing the likelihood function for the regression parameters. The proposed method outperforms the complete case analysis in simulation studies, which simply eliminates the subjects with censored terminal event times. Desirable asymptotic properties are provided.


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

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