Comparar métodos

	Modelo de Riscos Proporcionais de Cox Bayesiano	Análise de Sobrevida
Área	Epidemiologia	Estatística para pesquisa
Família	Process / pipeline	Process / pipeline
Ano de origem	1972 (Cox); Bayesian formulation developed through the 1990s	1958
Autor original	D. R. Cox (frequentist CPH, 1972); Bayesian extensions by Joseph Ibrahim, Ming-Hui Chen, Debajyoti Sinha (1990s–2001)	Edward L. Kaplan and Paul Meier
Tipo	Bayesian semiparametric survival regression	Method
Fonte seminal	Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Outros nomes	Bayesian CPH, Bayesian survival regression, Bayesian semiparametric hazard model, Bayesian partial likelihood survival model	Kaplan-Meier analysis, Cox regression, TTE analysis
Relacionados	4	3
Resumo	The Bayesian Cox proportional hazards model combines Cox's classical semiparametric survival regression with Bayesian inference, replacing point estimates and p-values with full posterior distributions over regression coefficients. It handles right-censored time-to-event outcomes, quantifies uncertainty about hazard ratios in probabilistic terms, and allows the incorporation of prior clinical or historical knowledge directly into the analysis.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.