Online Program Home
My Program

Abstract Details

Activity Number: 107
Type: Invited
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #318324 View Presentation
Title: Joint Modeling of Survival Time and Longitudinal Outcomes with Flexible Random Effects
Author(s): Jianwen Cai* and Jaeun Choi and Donglin Zeng and Andy Olshan
Companies: The University of North Carolina at Chapel Hill and Albert Einstein College of Medicine and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Keywords: Gaussian mixtures ; Generalized linear mixed model ; Maximum likelihood estimator ; Random effect ; Simultaneous modeling ; Stratified Cox proportional hazards model
Abstract:

In biomedical or public health research, it is common for both survival time and longitudinal outcomes to be collected for a subject, along with the subject's characteristics or risk factors. Joint analysis of longitudinal outcomes and survival time is used to find important variables for predicting both longitudinal outcomes and survival time which are correlated within the same subject. Random effects are introduced to account for the dependence between survival time and longitudinal outcomes due to unobserved factors. In this work, we assume the underlying distribution for the random effect to be unknown. We propose to use a mixture of Gaussian distributions as an approximation in the estimation. Weights of the mixture components are estimated with model parameters using the Expectation-Maximization (EM) algorithm. The observed information matrix is adopted to estimate the asymptotic variances of the proposed estimators. The method is demonstrated to perform well in finite samples via simulation studies. We illustrate our approach with data from a cancer study.


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

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association