手法を比較

	実用的多腕実験	実用的適応型試験
分野	実験計画法	実験計画法
系統	Process / pipeline	Process / pipeline
提唱年	1967 (pragmatic trial concept); multi-arm extensions 1990s–2000s	2000s–2010s (convergence period)
提唱者	Schwartz & Lellouch (pragmatic framing); extended to multi-arm settings in clinical and health services research	Synthesized from pragmatic trial tradition (Schwartz & Lellouch, 1967) and adaptive design methodology; formalized convergence in 2000s–2010s
種類	Experimental design	Hybrid experimental design
原典	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.	Pallmann, P., Bedding, A. W., Choodari-Oskooei, B., Dimairo, M., Flight, L., Hampson, L. V., ... & Sydes, M. R. (2018). Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Medicine, 16(1), 29.
別名	pragmatic multi-arm trial, multi-arm pragmatic RCT, pragmatic multi-treatment experiment, PMAT	pragmatic adaptive trial, real-world adaptive trial, PAT, adaptive pragmatic RCT
関連	6	3
概要	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 pragmatic adaptive experiment is a hybrid clinical trial design that combines the real-world generalizability of pragmatic trials with the statistical flexibility of adaptive designs. It enrolls a broad, representative patient population under routine care conditions, while using pre-specified interim analyses to modify trial parameters — such as sample size, allocation ratios, or arm selection — as outcome data accumulate. The result is a design that is both externally valid and resource-efficient.