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| Studio di coorte Bayesiano× | Analisi di sopravvivenza× | |
|---|---|---|
| Campo≠ | Epidemiologia | Statistica per la ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1990s–2000s (widespread adoption in epidemiology) | 1958 |
| Ideatore≠ | Bayesian framework: Thomas Bayes / Pierre-Simon Laplace; applied to cohort epidemiology from the 1990s onward | Edward L. Kaplan and Paul Meier |
| Tipo≠ | Observational longitudinal study with Bayesian inference | Method |
| Fonte seminale≠ | Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias≠ | Bayesian longitudinal cohort, Bayesian prospective cohort, Bayesian cohort analysis, Bayesian follow-up study | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | A Bayesian cohort study follows a defined group of individuals over time to estimate incidence, risk, or rate of outcomes, while using Bayesian statistical inference to incorporate prior knowledge and quantify uncertainty through posterior probability distributions rather than classical p-values and confidence intervals. It combines the longitudinal observational design of a cohort study with the probability-updating logic of Bayesian analysis, allowing richer uncertainty quantification and sequential updating as data accumulate. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
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