Módszerek összehasonlítása

	Dose-Escalation Design (Folyamatos Felülvizsgálati Módszer)	Bayes-féle következtetés
Tudományterület	Kísérlettervezés	Statisztika
Módszercsalád	Process / pipeline	Bayesian methods
Keletkezés éve	1990	1763
Megalkotó	John O'Quigley, Margaret Pepe & Lloyd Fisher	Thomas Bayes; Pierre-Simon Laplace
Típus	Adaptive Bayesian dose-finding design	Probabilistic inference paradigm
Alapmű	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.
Alternatív nevek	Continual Reassessment Method, CRM Design, Phase I Dose-Finding Design, Doz Artırma Tasarımı	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Kapcsolódó	2	3
Összefoglaló	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.