Lifetime Data Analysis

, Volume 20, Issue 4, pp 599–618 | Cite as

Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations

  • Sy Han Chiou
  • Sangwook Kang
  • Junghi Kim
  • Jun YanEmail author


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.


Buckley-James estimator Efficiency Induced smoothing Least squares Multivariate survival 


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Sy Han Chiou
    • 1
  • Sangwook Kang
    • 2
  • Junghi Kim
    • 3
  • Jun Yan
    • 4
    • 5
    • 6
    Email author
  1. 1.Department of Mathematics and StatisticsUniversity of Minnesota, DuluthDuluthUSA
  2. 2.Department of Applied StatisticsYonsei UniversitySeoulKorea
  3. 3.Division of BiostatisticsUniversity of MinnesotaMinneapolisUSA
  4. 4.Department of StatisticsUniversity of ConnecticutStorrsUSA
  5. 5.Center for Public Health and Health Policy ResearchUniversity of Connecticut Health CenterEast HartfordUSA
  6. 6.Center for Environmental Sciences & EngineeringUniversity of ConnecticutStorrsUSA

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