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| 군집 무작위 요인 배치 실험 (Cluster Randomized Fractional Factorial Experiment)× | 다중 팔 실험× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1950s (fractional factorial); 1980s-1990s (cluster-randomized extensions) | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| 창시자≠ | Box, Hunter & Hunter (fractional factorial foundations); Murray & colleagues (group-randomized trial methodology) | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| 유형≠ | Experimental design (compound) | Experimental design |
| 원전≠ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 | Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256. DOI ↗ |
| 별칭 | CR-FFE, cluster-randomized fractional factorial design, group-randomized fractional factorial trial, CRFFD | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| 관련 | 5 | 5 |
| 요약≠ | 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 multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies. |
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