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| Adaptív Többkaros Kísérlet× | Többkarú kísérlet× | |
|---|---|---|
| Tudományterület | Kísérlettervezés | Kísérlettervezés |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 2000s–2010s (MAMS framework formalized c. 2003–2011) | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Megalkotó≠ | Patrick Royston, Mahesh Parmar, and colleagues (multi-arm multi-stage framework); further developed by James Wason, Thomas Jaki and others | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Típus | Experimental design | Experimental design |
| Alapmű | 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 ↗ | 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 ↗ |
| Alternatív nevek | MAMS design, multi-arm adaptive trial, adaptive platform trial, response-adaptive multi-arm experiment | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Kapcsolódó≠ | 3 | 5 |
| Összefoglaló≠ | 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. | 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. |
| ScholarGateAdatkészlet ↗ |
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