Methoden vergleichen
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| Adaptives Laborexperiment× | Mehrarmiges Experiment× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1947 (sequential analysis foundations); adaptive laboratory applications widespread from 1990s | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Urheber≠ | Rooted in sequential analysis (Abraham Wald, 1947); adaptive clinical/lab designs formalized by Berry and colleagues (1990s–2000s) | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Typ≠ | Adaptive experimental design | Experimental design |
| Wegweisende Quelle≠ | Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36. DOI ↗ | Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256. DOI ↗ |
| Aliasnamen | adaptive lab experiment, sequential adaptive laboratory study, response-adaptive laboratory design, adaptive experimental laboratory design | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | An adaptive laboratory experiment is a controlled experimental design conducted in a laboratory setting where pre-specified decision rules allow modifications to the study — such as sample size, treatment allocation, or stopping criteria — based on accumulating data. Unlike fixed designs, adaptive designs incorporate planned interim analyses that permit the experiment to respond to emerging evidence while maintaining statistical validity and Type I error control. | A multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies. |
| ScholarGateDatensatz ↗ |
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