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| Sperimentazione Clinica Bayesiana di Fase III× | Sperimentazione Clinica Randomizzata Bayesiana× | |
|---|---|---|
| Campo | Epidemiologia | Epidemiologia |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1990s–2000s (widespread application) | 1980s–2000s (formal methodology consolidated ~2004–2006) |
| Ideatore≠ | Donald A. Berry; David J. Spiegelhalter (formalization in clinical context) | Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design) |
| Tipo≠ | Confirmatory randomized controlled trial with Bayesian inference | Randomized experimental study with Bayesian inference |
| 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 | 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 confirmatory trial, Bayesian RCT Phase III, Bayesian pivotal trial, BayesCT | Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT |
| Correlati | 5 | 5 |
| Sintesi≠ | A Bayesian Phase III clinical trial is a large-scale, confirmatory randomized controlled trial that uses Bayesian statistical inference rather than conventional frequentist hypothesis testing to evaluate whether an experimental treatment meets pre-defined efficacy and safety thresholds. By combining prior evidence with accumulating trial data, it quantifies the probability that the treatment effect exceeds a clinically meaningful threshold, enabling more transparent decision-making under uncertainty. | 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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