Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Expérience factorielle à plusieurs bras× | Expérimentation adaptative à plusieurs bras× | |
|---|---|---|
| Domaine | Plans d'expériences | Plans d'expériences |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1926 (factorial basis); multi-arm factorial trials formalized 1980s–1990s | 2000s–2010s (MAMS framework formalized c. 2003–2011) |
| Auteur d'origine≠ | R. A. Fisher (factorial foundations); multi-arm extension established in clinical trial methodology | Patrick Royston, Mahesh Parmar, and colleagues (multi-arm multi-stage framework); further developed by James Wason, Thomas Jaki and others |
| Type | Experimental design | Experimental design |
| Source fondatrice≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 | 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 | multi-arm factorial trial, factorial multi-arm trial, multi-arm factorial experiment, MAFT | MAMS design, multi-arm adaptive trial, adaptive platform trial, response-adaptive multi-arm experiment |
| Apparentées≠ | 6 | 3 |
| Résumé≠ | 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. | 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. |
| ScholarGateJeu de données ↗ |
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