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| Klynge-randomiseret kontrolleret forsøg× | Faktorialt randomiseret kontrolleret forsøg× | |
|---|---|---|
| Fagområde | Forsøgsdesign | Forsøgsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1978–1980s | 1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization) |
| Ophavsperson≠ | Cornfield (1978); systematised by Donner and colleagues (1980s) | R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014) |
| Type≠ | Experimental design | Experimental trial design |
| Oprindelig kilde≠ | Donner, A., & Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold. ISBN: 978-0340652978 | Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504. DOI ↗ |
| Aliasser | cluster RCT, group-randomized trial, community randomized trial, cluster-randomized experiment | Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization |
| Relaterede≠ | 4 | 6 |
| Resumé≠ | A cluster randomized controlled trial (cluster RCT) is an experimental design in which intact social or organisational groups — such as schools, clinics, villages, or workplaces — are randomly assigned to treatment conditions rather than individual participants. Outcomes are still measured at the individual level, but the unit of randomization is the cluster. This design is essential when an intervention is delivered to whole groups, when there is a risk of contamination between participants in the same setting, or when individual randomization is logistically or ethically impractical. | A factorial randomized controlled trial (factorial RCT) is an experimental design in which participants are randomly assigned to every possible combination of two or more independent factors (treatments or intervention components) simultaneously. This allows researchers to estimate the main effect of each factor and their interactions within a single, efficient trial, rather than running separate experiments for each factor. |
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