手法を比較

	クラスターランダム化フラクショナル要因実験	要因ランダム化比較試験（Factorial Randomized Controlled Trial）
分野	実験計画法	実験計画法
系統	Process / pipeline	Process / pipeline
提唱年	1950s (fractional factorial); 1980s-1990s (cluster-randomized extensions)	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
提唱者	Box, Hunter & Hunter (fractional factorial foundations); Murray & colleagues (group-randomized trial methodology)	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
種類	Experimental design (compound)	Experimental trial design
原典	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130	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.
別名	CR-FFE, cluster-randomized fractional factorial design, group-randomized fractional factorial trial, CRFFD	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
関連	5	6
概要	A cluster-randomized fractional factorial experiment combines two design principles: randomization is applied to intact groups (clusters such as schools, clinics, or communities) rather than individuals, and only a carefully chosen fraction of all possible factor-level combinations is tested. This pairing makes it practical to screen or evaluate multiple intervention components simultaneously in settings where individual randomization is infeasible, while keeping the number of required clusters manageable.	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.