Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations

Sy Han Chiou, Sangwook Kang, Junghi Kim, Jun Yan

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rank-based estimator with Gehan’s weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the within-cluster dependence, especially when the within-cluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.

Original languageEnglish
Pages (from-to)599-618
Number of pages20
JournalLifetime Data Analysis
Volume20
Issue number4
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Accelerated Failure Time Model
Generalized Estimating Equations
Multivariate Models
Estimator
Censored Data
Regression Coefficient
Margin
Smoothing
Multiplier Method
Failure Time Data
Resampling Methods
Least Squares Estimation
Relative Risk
Misspecification
Estimating Equation
Semiparametric Model
Covariance Structure
Simulation Study

All Science Journal Classification (ASJC) codes

  • Applied Mathematics

Cite this

@article{a49b43b576a04396ae79d9bb51fbf588,
title = "Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations",
abstract = "The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rank-based estimator with Gehan’s weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the within-cluster dependence, especially when the within-cluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.",
author = "Chiou, {Sy Han} and Sangwook Kang and Junghi Kim and Jun Yan",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/s10985-014-9292-x",
language = "English",
volume = "20",
pages = "599--618",
journal = "Lifetime Data Analysis",
issn = "1380-7870",
publisher = "Springer Netherlands",
number = "4",

}

Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations. / Chiou, Sy Han; Kang, Sangwook; Kim, Junghi; Yan, Jun.

In: Lifetime Data Analysis, Vol. 20, No. 4, 01.01.2014, p. 599-618.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations

AU - Chiou, Sy Han

AU - Kang, Sangwook

AU - Kim, Junghi

AU - Yan, Jun

PY - 2014/1/1

Y1 - 2014/1/1

N2 - The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rank-based estimator with Gehan’s weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the within-cluster dependence, especially when the within-cluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.

AB - The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rank-based estimator with Gehan’s weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the within-cluster dependence, especially when the within-cluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.

UR - http://www.scopus.com/inward/record.url?scp=84893851581&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893851581&partnerID=8YFLogxK

U2 - 10.1007/s10985-014-9292-x

DO - 10.1007/s10985-014-9292-x

M3 - Article

C2 - 24549607

AN - SCOPUS:84893851581

VL - 20

SP - 599

EP - 618

JO - Lifetime Data Analysis

JF - Lifetime Data Analysis

SN - 1380-7870

IS - 4

ER -