Σύγκριση μεθόδων

	Πραγματικό πείραμα πολλαπλών βραχιόνων	Πειραματικός Σχεδιασμός Πολλαπλών Βραχιόνων με Προσαρμογή
Πεδίο	Πειραματικός Σχεδιασμός	Πειραματικός Σχεδιασμός
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1967 (pragmatic trial concept); multi-arm extensions 1990s–2000s	2000s–2010s (MAMS framework formalized c. 2003–2011)
Δημιουργός	Schwartz & Lellouch (pragmatic framing); extended to multi-arm settings in clinical and health services research	Patrick Royston, Mahesh Parmar, and colleagues (multi-arm multi-stage framework); further developed by James Wason, Thomas Jaki and others
Τύπος	Experimental design	Experimental design
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	pragmatic multi-arm trial, multi-arm pragmatic RCT, pragmatic multi-treatment experiment, PMAT	MAMS design, multi-arm adaptive trial, adaptive platform trial, response-adaptive multi-arm experiment
Συναφείς	6	3
Σύνοψη	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.	An adaptive multi-arm experiment simultaneously evaluates several treatment conditions against a common control and modifies the trial in real time based on accumulating data — dropping ineffective arms early, reallocating participants toward promising ones, or adjusting sample sizes — all while controlling error rates. The approach maximizes information gained per participant and reduces the time and cost required to identify effective treatments relative to running sequential separate trials.