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| 군집 무작위 요인 배치 실험 (Cluster Randomized Fractional Factorial Experiment)× | 적응형 실험× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1950s (fractional factorial); 1980s-1990s (cluster-randomized extensions) | 1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s |
| 창시자≠ | Box, Hunter & Hunter (fractional factorial foundations); Murray & colleagues (group-randomized trial methodology) | Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others |
| 유형≠ | Experimental design (compound) | Experimental research 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 | Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761 |
| 별칭 | CR-FFE, cluster-randomized fractional factorial design, group-randomized fractional factorial trial, CRFFD | adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization |
| 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. | An adaptive experiment is an experimental design in which pre-specified rules allow the protocol to be modified — such as reallocating participants to better-performing arms, stopping early for efficacy or futility, or changing sample size — based on accumulating interim data, while maintaining statistical validity. Adaptive designs are widely used in clinical trials, behavioural economics, and online platform testing to improve efficiency and ethics without sacrificing inferential rigour. |
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