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 language | English |
|---|---|
| Pages (from-to) | 599-618 |
| Number of pages | 20 |
| Journal | Lifetime Data Analysis |
| Volume | 20 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Applied Mathematics
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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 journal › Article
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 -