Bayesian Bivariate Cure Rate Models Using Copula Functions release_stob265cfzhrhkjmit25x3662m

by Jie Huang, Haiming Zhou, Nader Ebrahimi

Published in International Journal of Statistics and Probability by Canadian Center of Science and Education.

2022   Volume 11

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.
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Date   2022-03-09
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