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Activity Number: 377 - New Innovations and Challenges in HGLMs and H-Likelihood
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: WNAR
Abstract #300143
Title: Frailty Mean Residual Life Regression for Clustered Survival Data: a Hierarchical Quasi-Likelihood Method
Author(s): Liming Xiang* and Rui Huang and Il Do Ha
Companies: Nanyang Technological University and Nanyang Technological University and Pukyong National University
Keywords: Hierarchical likelihood; inverse probability censoring weighting; mean residual life regression; estimating equations; multi-center study; quasi-likelihood

Frailty models are widely used to model clustered survival data arising in multi-center clinical studies. In the literature, most existing frailty models are proportional hazards, additive hazards or AFT model based. We formulate a frailty model based on mean residual life regression to accommodate intracluster correlation and in the meantime provide easily understand and straightforward interpretation for the effects of prognostic factors on the expectation of the remaining lifetime. We develop a novel hierarchical quasi-likelihood approach by making use of the idea of hierarchical likelihood in the construction of the quasi-likelihood function, leading to hierarchical estimating equations. Simulation results show favourable performance of the method regardless of frailty distributions. The utility of the proposed methodology is illustrated by its application to the data from a multi-institutional study of breast cancer.

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

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