Compara mètodes

	Experiment factorial pragmàtic complet	Assaig controlat aleatoritzat factorial
Camp	Disseny experimental	Disseny experimental
Família	Process / pipeline	Process / pipeline
Any d'origen	1920s (factorial); 1967/2009 (pragmatic framework)	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
Autor original	Full factorial: R.A. Fisher (1920s); Pragmatic framing: Schwartz & Lellouch (1967), formalized by Thorpe et al. (2009)	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
Tipus	Experimental design	Experimental trial design
Font seminal	Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalmers, I. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.	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.
Àlies	pragmatic factorial trial, real-world full factorial design, effectiveness full factorial experiment, pragmatic 2^k experiment	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
Relacionats	6	6
Resum	A pragmatic full factorial experiment combines the complete crossing of all factor levels (the full factorial structure) with the broad eligibility criteria, flexible delivery, and real-world conditions of a pragmatic trial. Every possible combination of factors is tested simultaneously, yielding both main effects and all interaction effects, while deliberately relaxing strict laboratory controls to reflect how interventions actually operate in practice.	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.