مقایسهٔ روش‌ها

	آزمایش تطبیقی دوسوکور	آزمایش تطبیقی
حوزه	طراحی آزمایش	طراحی آزمایش
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	Conceptual roots 1970s–1990s; regulatory codification 2004–2019	1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s
پدیدآور	Formalized through FDA adaptive design guidance and work by Scott Berry, Donald Berry, and colleagues	Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others
نوع	Experimental design combining blinding and adaptive modification	Experimental research design
منبع بنیادین	U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.	Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761
نام‌های دیگر	double-blind adaptive design, blinded adaptive trial, double-blind adaptive RCT, adaptive double-blind study	adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization
مرتبط	4	5
خلاصه	A double-blind adaptive experiment combines two powerful design features: double-blinding, which conceals treatment assignment from both participants and outcome assessors to prevent bias, and adaptive modification, which allows pre-specified changes to the trial's course — such as sample size re-estimation, allocation ratio shifts, or arm dropping — based on accumulating interim data. The result is a rigorous, bias-protected design that can respond to emerging evidence without compromising inferential validity.	An adaptive experiment is an experimental design in which pre-specified rules allow the protocol to be modified — such as reallocating participants to better-performing arms, stopping early for efficacy or futility, or changing sample size — based on accumulating interim data, while maintaining statistical validity. Adaptive designs are widely used in clinical trials, behavioural economics, and online platform testing to improve efficiency and ethics without sacrificing inferential rigour.