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| 교차 적응 실험× | 크로스오버 요인 실험× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Late 1990s–2000s | 1920s–1960s (synthesis of factorial and crossover traditions) |
| 창시자≠ | Developed through convergence of crossover trial methodology (Senn, Williams) and adaptive design methods (Bauer, Köhne, Chow, Chang) | R. A. Fisher (factorial principles, 1920s); crossover integration developed in biostatistics through mid-20th century |
| 유형≠ | Experimental design — hybrid adaptive/crossover | Experimental design |
| 원전≠ | Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC. ISBN: 978-1584888468 | Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC. ISBN: 978-1439861424 |
| 별칭 | adaptive crossover trial, adaptive crossover design, crossover adaptive trial, ACE design | within-subject factorial design, repeated-measures factorial experiment, factorial crossover trial, crossover factorial trial |
| 관련 | 5 | 5 |
| 요약≠ | An adaptive crossover experiment combines the within-subject efficiency of crossover designs — where each participant receives multiple treatments in sequence — with pre-specified adaptive rules that allow trial parameters to be modified based on interim data. Each participant acts as their own control across treatment periods, while ongoing accumulating evidence can trigger pre-planned changes such as sample size re-estimation, treatment arm dropping, or allocation ratio adjustment, all governed by a formal adaptation plan to preserve inferential validity. | 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. |
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