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Frailty proportional mean residual life regression for clustered survival data: A hierarchical quasi-likelihood method.
Stat Med 2019SM

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.

Authors+Show Affiliations

School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore.School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore.Department of Statistics, Pukyong National University, Busan, South Korea.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31418907

Citation

Huang, Rui, et al. "Frailty Proportional Mean Residual Life Regression for Clustered Survival Data: a Hierarchical Quasi-likelihood Method." Statistics in Medicine, 2019.
Huang R, Xiang L, Ha ID. Frailty proportional mean residual life regression for clustered survival data: A hierarchical quasi-likelihood method. Stat Med. 2019.
Huang, R., Xiang, L., & Ha, I. D. (2019). Frailty proportional mean residual life regression for clustered survival data: A hierarchical quasi-likelihood method. Statistics in Medicine, doi:10.1002/sim.8338.
Huang R, Xiang L, Ha ID. Frailty Proportional Mean Residual Life Regression for Clustered Survival Data: a Hierarchical Quasi-likelihood Method. Stat Med. 2019 Aug 16; PubMed PMID: 31418907.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Frailty proportional mean residual life regression for clustered survival data: A hierarchical quasi-likelihood method. AU - Huang,Rui, AU - Xiang,Liming, AU - Ha,Il Do, Y1 - 2019/08/16/ PY - 2018/10/10/received PY - 2019/05/26/revised PY - 2019/07/14/accepted PY - 2019/8/17/entrez KW - hierarchical likelihood KW - inverse probability censoring weighting KW - mean residual life regression KW - method of estimating equations KW - multicenter study KW - quasi-likelihood JF - Statistics in medicine JO - Stat Med N2 - 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. SN - 1097-0258 UR - https://www.unboundmedicine.com/medline/citation/31418907/Frailty_proportional_mean_residual_life_regression_for_clustered_survival_data:_A_hierarchical_quasi-likelihood_method L2 - https://doi.org/10.1002/sim.8338 DB - PRIME DP - Unbound Medicine ER -