Modeling categorical covariates for lifetime data in the presence of cure fraction by Bayesian partition structures
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.
Year of publication: |
2014
|
---|---|
Authors: | Louzada, Francisco ; Castro, Mário de ; Tomazella, Vera ; Gonzales, Jhon F.B. |
Published in: |
Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 41.2014, 3, p. 622-634
|
Publisher: |
Taylor & Francis Journals |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Negative binomial Kumaraswamy-G cure rate regression model
D'Andrea, Amanda, (2018)
-
Negative binomial Kumaraswamy-G cure rate regression model
D´Andrea, Amanda, (2018)
-
A Poisson mixed model with nonnormal random effect distribution
Fabio, Lizandra C., (2012)
- More ...