So sánh phương pháp

	Thí nghiệm Đa Cánh Thực dụng	Thí nghiệm đa nhánh nhân tử
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1967 (pragmatic trial concept); multi-arm extensions 1990s–2000s	1926 (factorial basis); multi-arm factorial trials formalized 1980s–1990s
Người khởi xướng	Schwartz & Lellouch (pragmatic framing); extended to multi-arm settings in clinical and health services research	R. A. Fisher (factorial foundations); multi-arm extension established in clinical trial methodology
Loại	Experimental design	Experimental design
Công trình gốc	Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443
Tên gọi khác	pragmatic multi-arm trial, multi-arm pragmatic RCT, pragmatic multi-treatment experiment, PMAT	multi-arm factorial trial, factorial multi-arm trial, multi-arm factorial experiment, MAFT
Liên quan	6	6
Tóm tắt	A pragmatic multi-arm experiment is an experimental design that simultaneously compares three or more interventions (arms) under real-world conditions rather than tightly controlled laboratory settings. It combines the broad eligibility, flexible delivery, and effectiveness orientation of pragmatic trials with the statistical efficiency of multi-arm structures, allowing researchers to evaluate multiple treatments or treatment variants against each other or a control within a single study, minimizing the resources and time required relative to running separate pairwise trials.	A factorial multi-arm experiment simultaneously tests multiple factors (each at two or more levels) by assigning participants to distinct arms that represent unique combinations of those factors. This design efficiently estimates the independent main effects of each factor and their interactions, all within a single study — making it far more informative than running separate one-factor experiments.