So sánh phương pháp

	Thí nghiệm đa nhánh	Thiết kế thí nghiệm thích ứng
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs	1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s
Người khởi xướng	Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)	Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others
Loại	Experimental design	Experimental research design
Công trình gốc	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.	Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761
Tên gọi khác	multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design	adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization
Liên quan	5	5
Tóm tắt	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.	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.