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	Dose-Escalation Design (Continual Reassessment Method)	Bayesiansk inferens	Sekventiell / Gruppsekventiell studiedesign
Ämnesområde	Försöksplanering	Statistik	Försöksplanering
Familj	Process / pipeline	Bayesian methods	Hypothesis test
Ursprungsår	1990	1763	1979
Upphovsperson	John O'Quigley, Margaret Pepe & Lloyd Fisher	Thomas Bayes; Pierre-Simon Laplace	O'Brien & Fleming; Pocock; Lan & DeMets
Typ	Adaptive Bayesian dose-finding design	Probabilistic inference paradigm	Adaptive stopping trial design
Ursprungskälla	O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48.	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.
Alias	Continual Reassessment Method, CRM Design, Phase I Dose-Finding Design, Doz Artırma Tasarımı	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference	group sequential design, adaptive stopping design, Ardışık Deneme Tasarımı (Sequential / Group Sequential)
Närliggande	2	3	3
Sammanfattning	Dose-Escalation Design, formalized as the Continual Reassessment Method (CRM), is a Bayesian adaptive algorithm for identifying the Maximum Tolerated Dose (MTD) in Phase I clinical trials. Introduced by John O'Quigley, Margaret Pepe, and Lloyd Fisher in 1990, CRM treats dose-toxicity response as a parametric curve, updates a prior probability model after each patient's outcome, and assigns subsequent patients to the dose currently estimated closest to a pre-specified target toxicity probability.	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.