विधियों की तुलना करें

	अनुकूली यादृच्छिक नियंत्रित परीक्षण	फ़ैक्टोरियल यादृच्छिक नियंत्रित परीक्षण
क्षेत्र	प्रयोगात्मक अभिकल्प	प्रयोगात्मक अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1980s–2000s (formalized; earlier sequential testing roots from Wald, 1947)	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
प्रवर्तक	Donald Berry and others; foundational adaptive trial methods developed through 1980s–2000s biostatistics literature	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
प्रकार	Experimental design — adaptive variant of RCT	Experimental trial design
मौलिक स्रोत	Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC. ISBN: 978-1584887690	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.
उपनाम	Adaptive RCT, Response-adaptive RCT, Adaptive clinical trial, Platform trial	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
संबंधित	6	6
सारांश	An adaptive randomized controlled trial (adaptive RCT) is an experimental design in which pre-specified rules allow modifications to the trial while it is ongoing — such as changing allocation ratios, dropping underperforming arms, or stopping early for efficacy or futility — based on accumulating interim data. These adaptations are planned before the trial starts and governed by statistical rules to preserve Type I error control and validity.	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.