Comparar métodos

	Análise de Sobrevivência Bayesiana	Estimador de Sobrevivência de Kaplan-Meier	Regressão Paramétrica de Sobrevivência de Weibull
Área	Bayesiano	Análise de sobrevivência	Análise de sobrevivência
Família	Bayesian methods	Survival analysis	Survival analysis
Ano de origem	2001	1958	1951
Autor original	Ibrahim, Chen & Sinha	Kaplan, E. L. & Meier, P.	Waloddi Weibull
Tipo	Bayesian time-to-event model	Non-parametric survival estimator	Fully parametric survival regression model
Fonte seminal	Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Outros nomes	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model	product-limit estimator, km curve, kaplan-meier sağkalım analizi	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Relacionados	4	2	4
Resumo	Bayesian survival analysis applies Bayesian inference to time-to-event models — Cox proportional hazards, parametric (Weibull, exponential), and cure models. Formalised comprehensively by Ibrahim, Chen and Sinha (2001), the approach encodes prior knowledge about hazard rates and regression coefficients, then updates it with censored survival data to yield posterior hazard ratios and credible intervals rather than single point estimates.	The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.	Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.