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| Studio Clinico multicentrico di Fase III× | Sperimentazione Clinica Randomizzata Bayesiana× | |
|---|---|---|
| Campo | Epidemiologia | Epidemiologia |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1940s–1990s (formalized through ICH harmonization ~1990s) | 1980s–2000s (formal methodology consolidated ~2004–2006) |
| Ideatore≠ | Codified through ICH E9 guideline (1998) and decades of regulatory practice (FDA, EMA) | Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design) |
| Tipo≠ | Confirmatory interventional study design | Randomized experimental study with Bayesian inference |
| Fonte seminale≠ | Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of Clinical Trials (5th ed.). Springer. ISBN: 978-3319185385 | Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756 |
| Alias | Phase III multicenter RCT, confirmatory multicenter trial, Phase 3 multicenter study, pivotal multicenter trial | Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | A multicenter Phase III clinical trial is the definitive confirmatory study that tests whether a new intervention produces a clinically meaningful benefit over a comparator in a large, representative patient population enrolled at two or more independent research sites. It is the primary evidence basis for regulatory approval by agencies such as the FDA and EMA, combining the statistical power of large samples with the external validity gained from geographic and demographic diversity across sites. | 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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