विधियों की तुलना करें

	डोज़-एस्केलेशन डिज़ाइन (निरंतर पुनर्मूल्यांकन विधि)	बायेसियन अनुमान
क्षेत्र	प्रयोगात्मक अभिकल्प	सांख्यिकी
परिवार	Process / pipeline	Bayesian methods
उद्भव वर्ष	1990	1763
प्रवर्तक	John O'Quigley, Margaret Pepe & Lloyd Fisher	Thomas Bayes; Pierre-Simon Laplace
प्रकार	Adaptive Bayesian dose-finding design	Probabilistic inference paradigm
मौलिक स्रोत	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.
उपनाम	Continual Reassessment Method, CRM Design, Phase I Dose-Finding Design, Doz Artırma Tasarımı	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
संबंधित	2	3
सारांश	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.