So sánh phương pháp

	Thí nghiệm thích ứng trong phòng thí nghiệm	Thí nghiệm đa nhánh
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1947 (sequential analysis foundations); adaptive laboratory applications widespread from 1990s	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
Người khởi xướng	Rooted in sequential analysis (Abraham Wald, 1947); adaptive clinical/lab designs formalized by Berry and colleagues (1990s–2000s)	Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)
Loại	Adaptive experimental design	Experimental design
Công trình gốc	Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.	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.
Tên gọi khác	adaptive lab experiment, sequential adaptive laboratory study, response-adaptive laboratory design, adaptive experimental laboratory design	multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design
Liên quan	5	5
Tóm tắt	An adaptive laboratory experiment is a controlled experimental design conducted in a laboratory setting where pre-specified decision rules allow modifications to the study — such as sample size, treatment allocation, or stopping criteria — based on accumulating data. Unlike fixed designs, adaptive designs incorporate planned interim analyses that permit the experiment to respond to emerging evidence while maintaining statistical validity and Type I error control.	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.