Compara mètodes

	Experiment pragmàtic multi-braç	Experiment multi-braç
Camp	Disseny experimental	Disseny experimental
Família	Process / pipeline	Process / pipeline
Any d'origen	1967 (pragmatic trial concept); multi-arm extensions 1990s–2000s	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
Autor original	Schwartz & Lellouch (pragmatic framing); extended to multi-arm settings in clinical and health services research	Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)
Tipus	Experimental design	Experimental design
Font seminal	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.
Àlies	pragmatic multi-arm trial, multi-arm pragmatic RCT, pragmatic multi-treatment experiment, PMAT	multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design
Relacionats	6	5
Resum	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 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.