Modeling categorical covariates for lifetime data in the presence of cure fraction by Bayesian partition structures

Francisco Louzada, Mário de Castro, Vera Tomazella, Jhon Franky Bernedo Gonzales

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this paper, we propose a Bayesian partition modeling for lifetime data in the presence of a cure fraction by considering a local structure generated by a tessellation which depends on covariates. In this modeling we include information of nominal qualitative variables with more than two categories or ordinal qualitative variables. The proposed modeling is based on a promotion time cure model structure but assuming that the number of competing causes follows a geometric distribution. It is an alternative modeling strategy to the conventional survival regression modeling generally used for modeling lifetime data in the presence of a cure fraction, which models the cure fraction through a (generalized) linear model of the covariates. An advantage of our approach is its ability to capture the effects of covariates in a local structure. The flexibility of having a local structure is crucial to capture local effects and features of the data. The modeling is illustrated on two real melanoma data sets.

Original languageEnglish
Pages (from-to)622-634
Number of pages13
JournalJournal of Applied Statistics
Volume41
Issue number3
DOIs
StatePublished - Mar 2014
Externally publishedYes

Bibliographical note

Funding Information:
The research was partially supported by the Brazilian Organizations FAPESP, CNPq and CAPES.

Keywords

  • Bayesian approach
  • categorical variable
  • cure fraction
  • geometric
  • MCMC
  • survival data
  • tessellation

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