Compară metode

	Design experimental unifactorial în cruce pentru subiect unic	Studiu N-de-1
Domeniu	Design experimental	Cercetare clinică
Familie	Process / pipeline	Process / pipeline
Anul apariției	1970s–1980s (single-case crossover formalized in behavioral research context)	1990s-2010s
Autorul original	Developed within the single-case research tradition; crossover application formalized by Barlow and Hersen and expanded by Kazdin	Kravitz, Duan, Vohra, and single-patient methodology pioneers
Tip	Experimental single-subject design	Research Design
Sursa seminală	Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 978-0195341881	Gabler, N. B., Duan, N., Vohra, S., & Kravitz, R. L. (2011). N-of-1 trials in the medical literature: a systematic review. Medical Care, 49(8), 761–768.
Denumiri alternative	crossover SSED, alternating-treatments crossover design, single-case crossover design, N-of-1 crossover design	single-patient RCT, n=1 trial, individual RCT, crossover n-of-1
Înrudite	4	3
Rezumat	The crossover single-subject experimental design (crossover SSED) applies two or more treatment conditions sequentially to the same individual, with a washout or return-to-baseline period between conditions. Because each participant serves as their own control, between-subject variability is eliminated, enabling precise causal inference about treatment effects even with very small samples — often a single participant. This design is widely used in applied behavior analysis, special education, rehabilitation, and clinical psychology.	An N-of-1 trial is a single-patient randomized controlled trial in which a patient alternates between treatment A and treatment B (or active drug and placebo) in repeated, randomized cross-over periods. Developed systematically in the 1990s–2010s by Kravitz, Duan, and Vohra, N-of-1 trials enable personalized medicine by determining which treatment works best for that specific individual, avoiding the assumption that population-average effects apply to all patients. They are ideal for chronic conditions with variable outcomes and heterogeneous treatment response.