Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Перекрестное рандомизированное контролируемое исследование× | Факториальное рандомизированное контролируемое исследование× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1960s (Grizzle 1965 for statistical foundations); widely used in clinical research since the 1970s | 1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization) |
| Автор метода≠ | Early formalized by statisticians including Bradford Hill and colleagues in clinical trials; theoretical framework developed by Grizzle (1965) and later Senn (2002) | R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014) |
| Тип≠ | Experimental within-subject design | Experimental trial design |
| Основополагающий источник≠ | Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley. ISBN: 978-0471496533 | Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504. DOI ↗ |
| Другие названия | crossover RCT, crossover trial, within-subject RCT, AB/BA crossover design | Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization |
| Связанные≠ | 5 | 6 |
| Сводка≠ | A crossover randomized controlled trial (crossover RCT) is an experimental design in which each participant receives all study interventions in a randomized sequence, separated by a washout period. Because every participant serves as their own control, within-subject variability is eliminated from the treatment comparison, yielding greater statistical power per participant than a parallel-group RCT of equal size. | A factorial randomized controlled trial (factorial RCT) is an experimental design in which participants are randomly assigned to every possible combination of two or more independent factors (treatments or intervention components) simultaneously. This allows researchers to estimate the main effect of each factor and their interactions within a single, efficient trial, rather than running separate experiments for each factor. |
| ScholarGateНабор данных ↗ |
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