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Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations.

Sy Han Chiou1, Sangwook Kang, Junghi Kim

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We developed a new method for analyzing survival data using multivariate accelerated failure time (AFT) models. This approach improves efficiency and handles complex data structures, making AFT models more practical for researchers.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • The Cox proportional hazards model is more common than the semiparametric accelerated failure time (AFT) model due to computational challenges.
  • Recent advancements in estimation techniques offer potential solutions for AFT model implementation.

Purpose of the Study:

  • To propose a generalized estimating equations (GEE) approach for multivariate accelerated failure time (AFT) models with censored data.
  • To enhance the practical utility and efficiency of AFT models in biostatistical analysis.

Main Methods:

  • Extension of generalized estimating equations (GEE) for censored data.
  • Utilizing induced smoothing for an initial rank-based estimator (Gehan's weight).
  • Multiplier resampling for variance estimation.

Main Results:

  • The proposed GEE approach provides consistent regression coefficient estimation, robust to working covariance misspecification.
  • The estimator demonstrated up to three times greater efficiency compared to methods ignoring within-cluster dependence in simulations.
  • The methodology was successfully applied to bivariate failure times data from a diabetic retinopathy study.

Conclusions:

  • The developed GEE approach offers a computationally feasible and efficient method for analyzing multivariate AFT models with censored data.
  • This method enhances the applicability of AFT models, particularly in studies with strong within-cluster dependence.
  • The findings support the use of this advanced statistical technique in complex survival data analysis.