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Bayesian Bivariate Cure Rate Models Using Copula Functions
2022
International Journal of Statistics and Probability
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
doi:10.5539/ijsp.v11n3p9
fatcat:stob265cfzhrhkjmit25x3662m