Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pragmatiskais randomizētais kontrolētais pētījums× | Faktoriālais randomizētais kontrolētais pētījums× | |
|---|---|---|
| Nozare | Eksperimentu plānošana | Eksperimentu plānošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1967 | 1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization) |
| Autors≠ | Daniel Schwartz and Joseph Lellouch | R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014) |
| Tips≠ | Experimental design — pragmatic trial | Experimental trial design |
| Pirmavots≠ | Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648. DOI ↗ | 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 ↗ |
| Citi nosaukumi | pRCT, pragmatic trial, practical clinical trial, real-world RCT | Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization |
| Saistītās≠ | 4 | 6 |
| Kopsavilkums≠ | A pragmatic randomized controlled trial (pRCT) tests whether an intervention works under ordinary, real-world conditions — broad eligibility, flexible delivery, and routine care settings. Participants are still randomly assigned to treatment or control, preserving causal inference, but the study is designed to reflect the diversity and variability of actual practice rather than the ideal conditions of an explanatory trial. The defining framework is the PRECIS-2 tool, which maps any RCT along nine pragmatic-to-explanatory dimensions. | 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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