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| Adaptives vollständiges faktorielles Experiment× | Adaptives Experiment× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1950s (factorial foundations); adaptive extensions prominent from 1990s onward | 1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s |
| Urheber≠ | Rooted in Box & Hunter factorial design tradition; adaptive extensions formalised by Atkinson, Donev and others in optimal design theory | Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others |
| Typ≠ | Experimental design | Experimental research design |
| Wegweisende Quelle≠ | Atkinson, A., Donev, A., & Tobias, R. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. ISBN: 978-0199296606 | Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761 |
| Aliasnamen | adaptive full-factorial design, sequential full factorial experiment, adaptive complete factorial design, dynamic full factorial trial | adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | An adaptive full factorial experiment is an experimental design that starts with a complete crossing of all factors and all their levels, then uses interim data to modify subsequent runs — dropping unpromising factor levels, adding new ones, or re-allocating replication — while preserving the full factorial structure within each phase. This integration of full factorial coverage with adaptive decision rules allows researchers to explore all main effects and interactions without committing to a fixed, inefficient run plan before any data are observed. | 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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