Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Bayesiaanse Cox Proportionele Risico's× | Kaplan-Meieranalyse× | |
|---|---|---|
| Vakgebied | Epidemiologie | Epidemiologie |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1972 (Cox); Bayesian formulation developed through the 1990s | 1958 |
| Grondlegger≠ | D. R. Cox (frequentist CPH, 1972); Bayesian extensions by Joseph Ibrahim, Ming-Hui Chen, Debajyoti Sinha (1990s–2001) | Edward L. Kaplan and Paul Meier |
| Type≠ | Bayesian semiparametric survival regression | Nonparametric survival estimator |
| Oorspronkelijke bron≠ | 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. DOI ↗ |
| Aliassen | Bayesian CPH, Bayesian survival regression, Bayesian semiparametric hazard model, Bayesian partial likelihood survival model | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Verwant≠ | 4 | 5 |
| Samenvatting≠ | 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. | Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research. |
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