Frailty proportional mean residual life regression for clustered survival data: A hierarchical quasi‐likelihood method. (16th August 2019)

• Record Type: Journal Article
• Authors: Huang, Rui; Xiang, Liming; Ha, Il Do
• Is Part Of: Statistics in medicine. Volume 38:Number 24(2019)
• Page Start: 4854
• Page End: 4870
• Publication Date: 2019-08-16
• Subjects: hierarchical likelihood -- inverse probability censoring weighting -- mean residual life regression -- method of estimating equations -- multicenter study -- quasi‐likelihood; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8338
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Frailty models are widely used to model clustered survival data arising in multicenter clinical studies. In the literature, most existing frailty models are proportional hazards, additive hazards, or accelerated failure time model based. In this paper, we propose a frailty model framework 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. To overcome estimation challenges, a novel hierarchical quasi‐likelihood approach is developed 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 favorable 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.