Сравнение методов

	Адаптивное рандомизированное клиническое исследование	Байесовское рандомизированное контролируемое исследование
Область	Эпидемиология	Эпидемиология
Семейство	Process / pipeline	Process / pipeline
Год появления	Late 1990s–2000s (widespread adoption post-2010)	1980s–2000s (formal methodology consolidated ~2004–2006)
Автор метода	Donald Berry and colleagues; formalized by FDA guidance in 2010 and 2019	Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)
Тип	Experimental clinical trial design	Randomized experimental study with Bayesian inference
Основополагающий источник	Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
Другие названия	adaptive RCT, adaptive trial design, response-adaptive randomization trial, adaptive clinical trial	Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT
Связанные	6	5
Сводка	An adaptive randomized clinical trial (adaptive RCT) is a prospective experimental study that uses pre-specified rules to modify one or more trial aspects — such as sample size, allocation ratios, or treatment arms — based on accumulating data collected during the trial itself, while maintaining statistical validity and integrity of the study.	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.