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

Francisco Louzada, M�rio de Castro, Vera Tomazella and Jhon F.B. Gonzales

Journal of Applied Statistics, 2014, vol. 41, issue 3, 622-634

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:41:y:2014:i:3:p:622-634

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DOI: 10.1080/02664763.2013.847067

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