Comparar métodos

	Modelo de Riscos Proporcionais de Cox Bayesiano	Ensaio Clínico Randomizado Bayesiano
Área	Epidemiologia	Epidemiologia
Família	Process / pipeline	Process / pipeline
Ano de origem	1972 (Cox); Bayesian formulation developed through the 1990s	1980s–2000s (formal methodology consolidated ~2004–2006)
Autor original	D. R. Cox (frequentist CPH, 1972); Bayesian extensions by Joseph Ibrahim, Ming-Hui Chen, Debajyoti Sinha (1990s–2001)	Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)
Tipo	Bayesian semiparametric survival regression	Randomized experimental study with Bayesian inference
Fonte seminal	Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
Outros nomes	Bayesian CPH, Bayesian survival regression, Bayesian semiparametric hazard model, Bayesian partial likelihood survival model	Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT
Relacionados	4	5
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.	A Bayesian randomized clinical trial (Bayesian RCT) combines the rigour of random treatment allocation with Bayesian statistical inference, allowing researchers to incorporate prior evidence and update beliefs continuously as trial data accumulate. Unlike the classical frequentist RCT, it yields direct probability statements about treatment effects and supports pre-specified adaptive stopping rules based on posterior probabilities.