เปรียบเทียบวิธี

	การทดลองทางคลินิกระยะที่สองแบบเบย์ (Bayesian Phase II Clinical Trial)	การทดลองทางคลินิกระยะที่สองแบบปรับเปลี่ยนได้
สาขาวิชา	ระบาดวิทยา	ระบาดวิทยา
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1990s (Thall & Simon 1994; Berry 1985–2006)	1994 (formal framework); widespread adoption 2000s–2010s
ผู้ริเริ่ม	Peter Thall, Richard Simon, Donald Berry (key contributors)	Peter Bauer & Klaus Kohne (formal statistical framework, 1994); broader adaptive trial methodology developed through FDA and ICH guidance in the 2000s
ประเภท	Interventional clinical trial design	Experimental clinical trial design
แหล่งต้นตำรับ	Thall, P. F., & Simon, R. (1994). Practical Bayesian guidelines for phase IIB clinical trials. Biometrics, 50(2), 337–349.	Bauer, P., & Kohne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics, 50(4), 1029–1041.
ชื่อเรียกอื่น	Bayesian phase 2 trial, Bayesian single-arm phase II study, Bayesian early-phase efficacy trial, Bayes phase II	Adaptive Ph II trial, seamless adaptive Phase II, adaptive dose-finding trial, response-adaptive Phase II
ที่เกี่ยวข้อง	6	1
สรุป	A Bayesian Phase II clinical trial applies Bayesian statistical inference to the standard Phase II objective of evaluating whether an experimental treatment shows sufficient early-phase efficacy to justify progression to a Phase III trial. By combining prior information with accumulating trial data, it enables principled interim monitoring, flexible stopping rules, and updated probability statements about treatment effect — all without the multiple-testing penalties that burden frequentist sequential designs.	An adaptive Phase II clinical trial is a prospective experimental design in which pre-specified rules allow the study protocol to be modified — such as dropping arms, adjusting sample size, or narrowing the patient population — based on accumulating interim data, without inflating the Type I error rate. The design is widely used in early-phase drug development to screen candidate doses or treatments efficiently while preserving statistical validity.