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| Байесов клиничен изпитване Фаза II× | Байесовски рандомизиран клиничен опит (Bayesian randomized clinical trial)× | |
|---|---|---|
| Област | Епидемиология | Епидемиология |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1990s (Thall & Simon 1994; Berry 1985–2006) | 1980s–2000s (formal methodology consolidated ~2004–2006) |
| Създател≠ | Peter Thall, Richard Simon, Donald Berry (key contributors) | Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design) |
| Тип≠ | Interventional clinical trial design | Randomized experimental study with Bayesian inference |
| Основополагащ източник≠ | Thall, P. F., & Simon, R. (1994). Practical Bayesian guidelines for phase IIB clinical trials. Biometrics, 50(2), 337–349. DOI ↗ | Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756 |
| Други названия | Bayesian phase 2 trial, Bayesian single-arm phase II study, Bayesian early-phase efficacy trial, Bayes phase II | Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT |
| Свързани≠ | 6 | 5 |
| Резюме≠ | A Bayesian Phase II clinical trial applies Bayesian statistical inference to the standard Phase II objective of evaluating whether an experimental treatment shows sufficient early-phase efficacy to justify progression to a Phase III trial. By combining prior information with accumulating trial data, it enables principled interim monitoring, flexible stopping rules, and updated probability statements about treatment effect — all without the multiple-testing penalties that burden frequentist sequential designs. | 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. |
| ScholarGateНабор от данни ↗ |
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