Võrdle meetodeid

	Double-blind adaptive experiment	Mitmeharuline eksperiment
Valdkond	Katsedisain	Katsedisain
Perekond	Process / pipeline	Process / pipeline
Tekkeaasta	Conceptual roots 1970s–1990s; regulatory codification 2004–2019	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
Looja	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)
Tüüp	Experimental design combining blinding and adaptive modification	Experimental design
Algallikas	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.
Rööpnimetused	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
Seotud	4	5
Kokkuvõte	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.