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Bayesian Bivariate Cure Rate Models Using Copula Functions

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  • Jie Huang
  • Haiming Zhou
  • Nader Ebrahimi

Abstract

Bivariate survival cure rate models extend the understanding of time-to-event data by allowing for a cured fraction of the population and dependence between paired units and make more accurate and informative conclusions. In this paper, we propose a Bayesian bivariate cure rate mode where a correlation coefficient is used for the association between bivariate cure rate fractions and a new generalized Farlie Gumbel Morgenstern (FGM) copula function is applied to model the dependence structure of bivariate survival times. For each marginal survival time, we apply a Weibull distribution, a log normal distribution, and a flexible three-parameter generalized extreme value (GEV) distribution to compare their performance. For the survival model fitting, DIC and LPML are used for model comparison. We perform a goodness-of-fit test for the new copula. Finally, we illustrate the performance of the proposed methods in simulated data and real data via Bayesian paradigm.

Suggested Citation

  • Jie Huang & Haiming Zhou & Nader Ebrahimi, 2022. "Bayesian Bivariate Cure Rate Models Using Copula Functions," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(3), pages 1-9, May.
    • Handle: RePEc:ibn:ijspjl:v:11:y:2022:i:3:p:9
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    References listed on IDEAS

    as
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    4. Lajmi Lakhal-Chaieb & Thierry Duchesne, 2017. "Association measures for bivariate failure times in the presence of a cure fraction," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 517-532, October.
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