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| Eksperyment wieloramienny krzyżowy× | Eksperyment adaptacyjny× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | Mid-20th century; multi-arm extensions formalized by 1970s–1980s | 1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s |
| Twórca≠ | Developed from early crossover trial methodology (Williams 1949; Cochran & Cox 1957) | Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others |
| Typ≠ | Within-subject experimental design with multiple treatment arms | Experimental research design |
| Źródło pierwotne≠ | Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584883869 | Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761 |
| Inne nazwy | multi-arm crossover trial, multi-period multi-treatment crossover, CMAT, multi-treatment crossover experiment | adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | A crossover multi-arm experiment is a within-subject experimental design in which each participant receives three or more treatments (arms) across successive periods, with random assignment to sequence. Because every participant experiences all arms, the design eliminates between-subject variability from treatment comparisons, dramatically increasing statistical power for a given sample size. It is widely used in clinical pharmacology, psychology, agriculture, and behavioral research. | 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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