Сравнение методов

	Байесовский вывод	Последовательный / групповой последовательный дизайн испытаний
Область	Статистика	Планирование эксперимента
Семейство	Bayesian methods	Hypothesis test
Год появления	1763	1979
Автор метода	Thomas Bayes; Pierre-Simon Laplace	O'Brien & Fleming; Pocock; Lan & DeMets
Тип	Probabilistic inference paradigm	Adaptive stopping trial design
Основополагающий источник	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.	O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.
Другие названия	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference	group sequential design, adaptive stopping design, Ardışık Deneme Tasarımı (Sequential / Group Sequential)
Связанные	3	3
Сводка	Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités.	Sequential and group sequential trial designs allow a study to be stopped early — or continued — based on interim analyses conducted as data accumulate. The core framework was formalised by O'Brien and Fleming in 1979 and extended by Lan and DeMets's alpha-spending approach, and it controls the overall Type I error rate across all planned looks by pre-specifying both efficacy and futility boundaries before enrolment begins.