Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Bayesian Survival Regression× | Bayes' Gemiddeld Effect Model× | |
|---|---|---|
| Vakgebied | Statistiek | Statistiek |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1990s–2001 | 1990s–2000s (modern Bayesian MCMC era) |
| Grondlegger≠ | Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al. | Gelman, Hill, and the broader Bayesian hierarchical modeling tradition |
| Type≠ | Bayesian parametric/semiparametric regression | Bayesian regression model |
| Oorspronkelijke bron≠ | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 | Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 |
| Aliassen | Bayesian time-to-event regression, Bayesian parametric survival model, Bayesian survival analysis, Bayesian accelerated failure time model | Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model |
| Verwant | 5 | 5 |
| Samenvatting≠ | Bayesian Survival Regression combines parametric or semiparametric survival models — such as Weibull, log-normal, or Cox proportional hazards — with Bayesian inference. Instead of point estimates, it produces full posterior distributions for regression coefficients and the baseline hazard, naturally handling censored observations and incorporating prior knowledge about event times or covariate effects. | The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for both population-level and group-level effects simultaneously. |
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