Сравнение методов

	Эксперимент с полным факторным дизайном	Перекрестное рандомизированное контролируемое исследование
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1920s–1960s (synthesis of factorial and crossover traditions)	1960s (Grizzle 1965 for statistical foundations); widely used in clinical research since the 1970s
Автор метода	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)
Тип	Experimental design	Experimental within-subject design
Основополагающий источник	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
Другие названия	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
Связанные	5	5
Сводка	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.