Comparer des méthodes

	Essai Contrôlé Randomisé Adaptatif	Inférence bayésienne
Domaine	Plans d'expériences	Statistique
Famille	Process / pipeline	Bayesian methods
Année d'origine	1980s–2000s (formalized; earlier sequential testing roots from Wald, 1947)	1763
Auteur d'origine	Donald Berry and others; foundational adaptive trial methods developed through 1980s–2000s biostatistics literature	Thomas Bayes; Pierre-Simon Laplace
Type	Experimental design — adaptive variant of RCT	Probabilistic inference paradigm
Source fondatrice	Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC. ISBN: 978-1584887690	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.
Alias	Adaptive RCT, Response-adaptive RCT, Adaptive clinical trial, Platform trial	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Apparentées	6	3
Résumé	An adaptive randomized controlled trial (adaptive RCT) is an experimental design in which pre-specified rules allow modifications to the trial while it is ongoing — such as changing allocation ratios, dropping underperforming arms, or stopping early for efficacy or futility — based on accumulating interim data. These adaptations are planned before the trial starts and governed by statistical rules to preserve Type I error control and validity.	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.