A Bayesian proportional hazards mixture cure model for interval-censored data.
Lifetime Data Anal
; 30(2): 327-344, 2024 Apr.
Article
em En
| MEDLINE
| ID: mdl-38015378
ABSTRACT
The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval-censored, the estimation of this model is challenging due to its complex data structure. In this article, we propose a computationally efficient semiparametric Bayesian approach, facilitated by spline approximation and Poisson data augmentation, for model estimation and inference with interval-censored data and a cure rate. The spline approximation and Poisson data augmentation greatly simplify the MCMC algorithm and enhance the convergence of the MCMC chains. The empirical properties of the proposed method are examined through extensive simulation studies and also compared with the R package "GORCure". The use of the proposed method is illustrated through analyzing a data set from the Aerobics Center Longitudinal Study.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Modelos Estatísticos
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article