Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Klasterizēts adaptīvais eksperiments× | Eksperiments ar vairākām grupām× | |
|---|---|---|
| Nozare | Eksperimentu plānošana | Eksperimentu plānošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2000s–2010s | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Autors≠ | Synthesised from cluster randomization methodology (Donner, 1978; Donner & Klar, 2000) and adaptive design frameworks (Bauer & Kohne, 1994; Pallmann et al., 2018) | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Tips | Experimental design | Experimental design |
| Pirmavots≠ | Hayes, R. J., & Moulton, L. H. (2017). Cluster Randomised Trials (2nd ed.). CRC Press / Chapman & Hall. ISBN: 978-1498728225 | 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 ↗ |
| Citi nosaukumi | adaptive cluster RCT, adaptive group-randomized trial, cluster adaptive design, adaptive cluster trial | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | A cluster randomized adaptive experiment combines two methodological principles: (1) intact groups such as schools, clinics, or villages are randomly assigned to treatment conditions rather than individuals, and (2) pre-specified rules allow the design to be modified during the trial based on accumulating cluster-level data. Adaptations may include dropping underperforming arms, reallocating clusters, or adjusting sample size, while maintaining statistical validity and controlling Type I error. | 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. |
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