Format

Send to

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

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

Author information

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.

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

PMID:
25123102
DOI:
10.1016/j.cmpb.2014.07.011
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center