পদ্ধতির তুলনা করুন

	ডাবল-ব্লাইন্ড অ্যাডাপ্টিভ এক্সপেরিমেন্ট	বহু-বাহু পরীক্ষা
ক্ষেত্র	পরীক্ষামূলক নকশা	পরীক্ষামূলক নকশা
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	Conceptual roots 1970s–1990s; regulatory codification 2004–2019	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
প্রবর্তক	Formalized through FDA adaptive design guidance and work by Scott Berry, Donald Berry, and colleagues	Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)
ধরন	Experimental design combining blinding and adaptive modification	Experimental design
মৌলিক উৎস	U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.	Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256.
অপর নাম	double-blind adaptive design, blinded adaptive trial, double-blind adaptive RCT, adaptive double-blind study	multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design
সম্পর্কিত	4	5
সারসংক্ষেপ	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.	A multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies.