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| Adaptives Kontrollgruppen-Studiendesign× | Adaptives Experiment× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1994 (formal adaptive framework); wider adoption 2000s–2010s | 1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s |
| Urheber≠ | Peter Bauer and Klaus Kohne (adaptive interim analysis framework, 1994); broader adaptive design methodology developed by Scott Chow and Mark Chang | Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others |
| Typ≠ | Adaptive experimental design | Experimental research design |
| Wegweisende Quelle≠ | Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886760 | Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761 |
| Aliasnamen | adaptive controlled experiment, adaptive two-arm controlled design, adaptive parallel-group design, flexible controlled trial design | adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | An adaptive control group experimental design is an experiment that assigns participants to at least one treatment arm and one concurrent control group, while allowing pre-specified modifications to the trial — such as sample size re-estimation, early stopping, or allocation ratio changes — based on accumulating data. Adaptations are governed by decision rules established before the study begins, preserving Type I error control while improving efficiency. | 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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