A Bayesian proportional hazards mixture cure model for interval-censored data.
Data augmentation
I-splines
Interval-censored data
Mixture cure model
Journal
Lifetime data analysis
ISSN: 1572-9249
Titre abrégé: Lifetime Data Anal
Pays: United States
ID NLM: 9516348
Informations de publication
Date de publication:
Apr 2024
Apr 2024
Historique:
received:
01
02
2023
accepted:
12
10
2023
pubmed:
28
11
2023
medline:
28
11
2023
entrez:
28
11
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 38015378
doi: 10.1007/s10985-023-09613-8
pii: 10.1007/s10985-023-09613-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
327-344Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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