Survival analysis of critically ill patients with cancer: use of semiparametric (transformation models) under a hierarchical Bayesian approach (2025)
- Authors:
- USP affiliated authors: ACHCAR, JORGE ALBERTO - FMRP ; BARILI, EMERSON - FMRP
- Unidade: FMRP
- DOI: 10.28951/bjb.v43i3.722
- Subjects: DADOS CENSURADOS; MÉTODOS MCMC; INFERÊNCIA BAYESIANA; SOBREVIDA; NEOPLASIAS
- Keywords: Semiparametric models; Censored data; Covariates; Hierarchical Bayesian analysis; MCMCmethods; Cancer survival times
- Language: Inglês
- Imprenta:
- Source:
- Título: Brazilian Journal of Biometrics
- ISSN: 2764-5290
- Volume/Número/Paginação/Ano: v. 43, art. e-43722, [23] p, 2025
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
BARILI, Emerson e ACHCAR, Jorge Alberto. Survival analysis of critically ill patients with cancer: use of semiparametric (transformation models) under a hierarchical Bayesian approach. Brazilian Journal of Biometrics, v. 43, 2025Tradução . . Disponível em: https://doi.org/10.28951/bjb.v43i3.722. Acesso em: 08 abr. 2026. -
APA
Barili, E., & Achcar, J. A. (2025). Survival analysis of critically ill patients with cancer: use of semiparametric (transformation models) under a hierarchical Bayesian approach. Brazilian Journal of Biometrics, 43. doi:10.28951/bjb.v43i3.722 -
NLM
Barili E, Achcar JA. Survival analysis of critically ill patients with cancer: use of semiparametric (transformation models) under a hierarchical Bayesian approach [Internet]. Brazilian Journal of Biometrics. 2025 ; 43[citado 2026 abr. 08 ] Available from: https://doi.org/10.28951/bjb.v43i3.722 -
Vancouver
Barili E, Achcar JA. Survival analysis of critically ill patients with cancer: use of semiparametric (transformation models) under a hierarchical Bayesian approach [Internet]. Brazilian Journal of Biometrics. 2025 ; 43[citado 2026 abr. 08 ] Available from: https://doi.org/10.28951/bjb.v43i3.722 - Semiparametric transformation model in presence of cure fraction: a hierarchical Bayesian approach assuming the unknown hazards as latent factors
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