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| Korsningsfaktor-experiment× | Kors-randomiserad kontrollerad studie× | |
|---|---|---|
| Ämnesområde | Försöksplanering | Försöksplanering |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1920s–1960s (synthesis of factorial and crossover traditions) | 1960s (Grizzle 1965 for statistical foundations); widely used in clinical research since the 1970s |
| Upphovsperson≠ | R. A. Fisher (factorial principles, 1920s); crossover integration developed in biostatistics through mid-20th century | Early formalized by statisticians including Bradford Hill and colleagues in clinical trials; theoretical framework developed by Grizzle (1965) and later Senn (2002) |
| Typ≠ | Experimental design | Experimental within-subject design |
| Ursprungskälla≠ | Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC. ISBN: 978-1439861424 | Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley. ISBN: 978-0471496533 |
| Alias | within-subject factorial design, repeated-measures factorial experiment, factorial crossover trial, crossover factorial trial | crossover RCT, crossover trial, within-subject RCT, AB/BA crossover design |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | 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 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. |
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