Сравнение на методи

	Прагматично рандомизирано контролирано проучване	Факториален рандомизиран контролиран експеримент
Област	Планиране на експеримента	Планиране на експеримента
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1967	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
Създател	Daniel Schwartz and Joseph Lellouch	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
Тип	Experimental design — pragmatic trial	Experimental trial design
Основополагащ източник	Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.	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.
Други названия	pRCT, pragmatic trial, practical clinical trial, real-world RCT	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
Свързани	4	6
Резюме	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.