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A Bayesian semiparametric accelerate failure time mixture cure model.

Yijun Wang1,2, Weiwei Wang1,2, Yincai Tang3

  • 1School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, 310018, People's Republic of China.

The International Journal of Biostatistics
|September 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian semiparametric approach for the accelerated failure time mixture cure (AFTMC) model. The method effectively estimates cure probability and survival distributions for uncured patients, showing comparable performance to parametric models.

Keywords:
Dirichlet processGibbs sampleraccelerated failure time mixture cure model

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Bayesian Statistics

Background:

  • The accelerated failure time mixture cure (AFTMC) model is crucial for analyzing survival data with a cured fraction.
  • Accurate estimation of cure probability and survival in uncured populations is essential for clinical interpretation.

Purpose of the Study:

  • To propose a novel Bayesian semiparametric method for the AFTMC model.
  • To estimate both cure probability and the survival distribution of uncured patients.
  • To provide an efficient and implementable computational approach.

Main Methods:

  • A Bayesian semiparametric approach is employed.
  • The baseline error distribution for uncured patients is nonparametrically modeled using a Dirichlet process mixture.
  • An efficient Gibbs sampler is developed using stick-breaking formulation, retrospective, and slice sampling techniques.

Main Results:

  • The proposed method provides accurate parameter and density estimations for the AFTMC model.
  • Simulation studies demonstrate performance comparable to fully parametric methods.
  • The approach was successfully applied to colorectal cancer clinical trial data.

Conclusions:

  • The developed Bayesian semiparametric method offers a flexible and effective tool for AFTMC models.
  • The method is computationally efficient and readily implementable in standard statistical software.
  • This approach enhances the analysis of survival data with a cured fraction in clinical research.