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| Studio caso-controllo Bayesiano× | Sperimentazione Clinica Randomizzata Bayesiana× | |
|---|---|---|
| Campo | Epidemiologia | Epidemiologia |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c. | 1980s–2000s (formal methodology consolidated ~2004–2006) |
| Ideatore≠ | Sander Greenland (Bayesian epidemiology formalization); earlier Bayesian logistic methods: Leonard (1972) | Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design) |
| Tipo≠ | Observational analytic study with Bayesian inference | Randomized experimental study with Bayesian inference |
| Fonte seminale≠ | Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765-775. DOI ↗ | Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756 |
| Alias | Bayesian case-control design, Bayesian odds ratio estimation, Bayesian matched case-control, Bayesian logistic regression case-control | Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | A Bayesian case-control study applies Bayesian statistical inference to the classic case-control epidemiological design, formally combining prior knowledge about exposure-disease associations with observed case and control data to estimate posterior odds ratios and credible intervals. Rather than relying solely on observed data, the Bayesian framework allows investigators to incorporate external evidence — from prior studies, expert knowledge, or mechanistic understanding — into the analysis, yielding probability statements about effect sizes that are often more interpretable than classical p-values and confidence intervals. | A Bayesian randomized clinical trial (Bayesian RCT) combines the rigour of random treatment allocation with Bayesian statistical inference, allowing researchers to incorporate prior evidence and update beliefs continuously as trial data accumulate. Unlike the classical frequentist RCT, it yields direct probability statements about treatment effects and supports pre-specified adaptive stopping rules based on posterior probabilities. |
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