Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Адаптивное рандомизированное контролируемое исследование× | Многорукавный эксперимент× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1980s–2000s (formalized; earlier sequential testing roots from Wald, 1947) | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Автор метода≠ | Donald Berry and others; foundational adaptive trial methods developed through 1980s–2000s biostatistics literature | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Тип≠ | Experimental design — adaptive variant of RCT | Experimental design |
| Основополагающий источник≠ | Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC. ISBN: 978-1584887690 | 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 ↗ |
| Другие названия | Adaptive RCT, Response-adaptive RCT, Adaptive clinical trial, Platform trial | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Связанные≠ | 6 | 5 |
| Сводка≠ | An adaptive randomized controlled trial (adaptive RCT) is an experimental design in which pre-specified rules allow modifications to the trial while it is ongoing — such as changing allocation ratios, dropping underperforming arms, or stopping early for efficacy or futility — based on accumulating interim data. These adaptations are planned before the trial starts and governed by statistical rules to preserve Type I error control and validity. | 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. |
| ScholarGateНабор данных ↗ |
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