Comparar métodos

	Experimento adaptativo doble ciego	Experimento multibranzo
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	Conceptual roots 1970s–1990s; regulatory codification 2004–2019	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
Autor original	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)
Tipo	Experimental design combining blinding and adaptive modification	Experimental design
Fuente seminal	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.
Alias	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
Relacionados	4	5
Resumen	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.