Multivariate survival data under bivariate frailty: An estimating equation approach

Xiaonan Xue

doi:10.2307/2533687

Multivariate survival data under bivariate frailty: An estimating equation approach

Xiaonan Xue

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

A modified frailty model is developed to improve the computing efficiency of the bivariate frailty model proposed by Xue and Brookmeyer (1996, Lifetime Data Analysis 2, 277-290) for the analysis of multivariate survival data. Originally, the frailty was modeled parametrically and a modified EM approach was used to estimate the parameters of interest, however, with intensive computations. The modified frailty model formulates a Poisson regression model and applies quasi-likelihood estimating equations to estimate the parameters of interest. This procedure not only significantly reduces the computation but also avoids using a parametric assumption for the frailty distribution. Simulation studies show the estimators perform well. The method is also applied to a mental health care dataset.

Original language	English (US)
Pages (from-to)	1631-1637
Number of pages	7
Journal	Biometrics
Volume	54
Issue number	4
DOIs	https://doi.org/10.2307/2533687
State	Published - 1998
Externally published	Yes

Keywords

Heterogeneity
Poisson formulation
Quasi-likelihood

ASJC Scopus subject areas

Statistics and Probability
General Biochemistry, Genetics and Molecular Biology
General Immunology and Microbiology
General Agricultural and Biological Sciences
Applied Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2307/2533687

Cite this

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title = "Multivariate survival data under bivariate frailty: An estimating equation approach",

abstract = "A modified frailty model is developed to improve the computing efficiency of the bivariate frailty model proposed by Xue and Brookmeyer (1996, Lifetime Data Analysis 2, 277-290) for the analysis of multivariate survival data. Originally, the frailty was modeled parametrically and a modified EM approach was used to estimate the parameters of interest, however, with intensive computations. The modified frailty model formulates a Poisson regression model and applies quasi-likelihood estimating equations to estimate the parameters of interest. This procedure not only significantly reduces the computation but also avoids using a parametric assumption for the frailty distribution. Simulation studies show the estimators perform well. The method is also applied to a mental health care dataset.",

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year = "1998",

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T1 - Multivariate survival data under bivariate frailty

T2 - An estimating equation approach

AU - Xue, Xiaonan

PY - 1998

Y1 - 1998

N2 - A modified frailty model is developed to improve the computing efficiency of the bivariate frailty model proposed by Xue and Brookmeyer (1996, Lifetime Data Analysis 2, 277-290) for the analysis of multivariate survival data. Originally, the frailty was modeled parametrically and a modified EM approach was used to estimate the parameters of interest, however, with intensive computations. The modified frailty model formulates a Poisson regression model and applies quasi-likelihood estimating equations to estimate the parameters of interest. This procedure not only significantly reduces the computation but also avoids using a parametric assumption for the frailty distribution. Simulation studies show the estimators perform well. The method is also applied to a mental health care dataset.

AB - A modified frailty model is developed to improve the computing efficiency of the bivariate frailty model proposed by Xue and Brookmeyer (1996, Lifetime Data Analysis 2, 277-290) for the analysis of multivariate survival data. Originally, the frailty was modeled parametrically and a modified EM approach was used to estimate the parameters of interest, however, with intensive computations. The modified frailty model formulates a Poisson regression model and applies quasi-likelihood estimating equations to estimate the parameters of interest. This procedure not only significantly reduces the computation but also avoids using a parametric assumption for the frailty distribution. Simulation studies show the estimators perform well. The method is also applied to a mental health care dataset.

KW - Heterogeneity

KW - Poisson formulation

KW - Quasi-likelihood

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UR - https://www.scopus.com/pages/publications/0032416632#tab=citedBy

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DO - 10.2307/2533687

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AN - SCOPUS:0032416632

SN - 0006-341X

VL - 54

SP - 1631

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JO - Biometrics

JF - Biometrics

IS - 4

ER -

Multivariate survival data under bivariate frailty: An estimating equation approach

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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