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| Eksperyment czynnikowy krzyżowy× | Eksperyment losowy kontrolowany z czynnikami× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1920s–1960s (synthesis of factorial and crossover traditions) | 1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization) |
| Twórca≠ | R. A. Fisher (factorial principles, 1920s); crossover integration developed in biostatistics through mid-20th century | R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014) |
| Typ≠ | Experimental design | Experimental trial design |
| Źródło pierwotne≠ | Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC. ISBN: 978-1439861424 | 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 ↗ |
| Inne nazwy | within-subject factorial design, repeated-measures factorial experiment, factorial crossover trial, crossover factorial trial | Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization |
| Pokrewne≠ | 5 | 6 |
| Podsumowanie≠ | A crossover factorial experiment combines two powerful design principles: factorial structure, which studies multiple factors and their interactions simultaneously, and crossover structure, in which each participant receives more than one treatment combination across sequential periods. By serving as their own control, participants reduce between-subject variability, improving statistical power while also revealing how different factor levels interact within the same individual. | 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. |
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