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| Esperimento pragmatico multi-braccio× | Esperimento Adattivo a Bracci Multipli× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1967 (pragmatic trial concept); multi-arm extensions 1990s–2000s | 2000s–2010s (MAMS framework formalized c. 2003–2011) |
| Ideatore≠ | 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 |
| Tipo | Experimental design | Experimental design |
| Fonte seminale≠ | 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. DOI ↗ | 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. DOI ↗ |
| Alias | 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 |
| Correlati≠ | 6 | 3 |
| Sintesi≠ | 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. |
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