Bayesian bivariate generalized Lindley model for survival data with a cure fraction

Edson Z Martinez; Jorge A Achcar

doi:10.1016/j.cmpb.2014.07.011

Bayesian bivariate generalized Lindley model for survival data with a cure fraction

Comput Methods Programs Biomed. 2014 Nov;117(2):145-57. doi: 10.1016/j.cmpb.2014.07.011. Epub 2014 Aug 1.

Authors

Edson Z Martinez¹, Jorge A Achcar²

Affiliations

¹ Department of Social Medicine, University of São Paulo (USP), Ribeirão Preto School of Medicine, Brazil. Electronic address: edson@fmrp.usp.br.
² Department of Social Medicine, University of São Paulo (USP), Ribeirão Preto School of Medicine, Brazil.

PMID: 25123102
DOI: 10.1016/j.cmpb.2014.07.011

Abstract

The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest. In this article, we introduce a bivariate model for survival data with a cure fraction based on the three-parameter generalized Lindley distribution. The joint distribution of the survival times is obtained by using copula functions. We consider three types of copula function models, the Farlie-Gumbel-Morgenstern (FGM), Clayton and Gumbel-Barnett copulas. The model is implemented under a Bayesian framework, where the parameter estimation is based on Markov Chain Monte Carlo (MCMC) techniques. To illustrate the utility of the model, we consider an application to a real data set related to an invasive cervical cancer study.

Keywords: Bayesian analysis; Copula function; Cure fraction model; Lindley distribution; Survival analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Bayes Theorem
Computer Simulation
Data Interpretation, Statistical*
Female
Humans
Incidence
Models, Statistical*
Mortality*
Pattern Recognition, Automated / methods*
Risk Factors
Survival Analysis*
Uterine Cervical Neoplasms / mortality*