手法を比較

	Blocked Randomized Controlled Trial	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	1920s (Fisher's blocking principle); applied to RCTs from the 1940s onward	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
提唱者	R. A. Fisher (blocking principle); systematic RCT application by Bradford Hill and later Pocock, Friedman et al.	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
種類	Experimental design	Experimental trial design
原典	Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2010). Fundamentals of Clinical Trials (4th ed.). Springer. ISBN: 978-1441915856	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	blocked RCT, block-randomized trial, stratified block randomization trial, permuted block randomization	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
関連	5	6
概要	A blocked randomized controlled trial (blocked RCT) uses permuted-block randomization to ensure that treatment groups remain balanced in size — and optionally in key characteristics — throughout recruitment. Within each block of fixed or randomly varied size, all treatment allocations are present in equal numbers, so imbalance cannot accumulate even if the trial is stopped early. This makes blocked RCTs the standard randomization approach in clinical and behavioral intervention research.	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.