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Activity Number: 417 - Recent advancement on life time data analysis
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: International Chinese Statistical Association
Abstract #317800
Title: Joint Modeling of Survival and Longitudinal Data Under the Proportional Mean Residual Model
Author(s): Ruiwen Zhou* and Jianguo Sun
Companies: Univerisity of Missouri-Columbia and Univerisity of Missouri-Columbia
Keywords: Proportional Mean Residual Model; Penalized Quasi Likelihood; Shared Parameter Model; Joint Modeling; Random Effect

This paper considers joint regression analysis of longitudinal and survival data under the shared latent variable framework. Many authors have discussed the joint analysis problem but most of the existing methods are the hazard-based approach with respect to the failure time variable of interest. It is well-known that sometimes the mean residual life (MRL) model, which measures the remaining life expectancy, may be of more interest. In this paper, we propose an MRL-based method for the joint analysis, which gives a meaningful and informative alternative to the hazard-based approach. In the method, the proportional mean residual model and the generalized linear mixed model are employed to model the failure time of interest and the longitudinal variable, respectively. For estimation, a penalized quasi-likelihood approach is developed with the use of Laplace approximation. A simulation study is conducted and the proposed method is applied to a set of real data.

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

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