Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise de Kaplan-Meier Bayesiana× | Modelo de Riscos Proporcionais de Cox Bayesiano× | |
|---|---|---|
| Área | Epidemiologia | Epidemiologia |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1976 | 1972 (Cox); Bayesian formulation developed through the 1990s |
| Autor original≠ | Susarla & Van Ryzin (Bayesian nonparametric survival estimation) | D. R. Cox (frequentist CPH, 1972); Bayesian extensions by Joseph Ibrahim, Ming-Hui Chen, Debajyoti Sinha (1990s–2001) |
| Tipo≠ | Bayesian nonparametric survival analysis | Bayesian semiparametric survival regression |
| Fonte seminal≠ | Susarla, V., & Van Ryzin, J. (1976). Nonparametric Bayesian estimation of survival curves from incomplete observations. Journal of the American Statistical Association, 71(356), 897–902. DOI ↗ | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 |
| Outros nomes | Bayesian survival curve estimation, Bayesian nonparametric survival analysis, Dirichlet process Kaplan-Meier, BKM | Bayesian CPH, Bayesian survival regression, Bayesian semiparametric hazard model, Bayesian partial likelihood survival model |
| Relacionados | 4 | 4 |
| Resumo≠ | Bayesian Kaplan-Meier analysis extends the classical Kaplan-Meier estimator by placing a prior distribution over the survival function and updating it with observed time-to-event data to obtain a full posterior distribution for the survival curve. This approach, rooted in Susarla and Van Ryzin's 1976 Dirichlet-process framework, yields credible intervals rather than confidence intervals and enables coherent incorporation of prior clinical knowledge, making it particularly valuable in small-sample or early-phase clinical settings. | 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. |
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