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	Adaptiv enpersonsdesign	N-of-1-studie
Ämnesområde	Försöksplanering	Klinisk forskning
Familj	Process / pipeline	Process / pipeline
Ursprungsår	Classical SSED: 1960s–1970s; adaptive extensions formalised: 2000s–2010s	1990s-2010s
Upphovsperson	Evolved from classical single-case designs (Skinner, Sidman); adaptive features formalised in clinical N-of-1 literature (Zucker, Schmid, Nikles et al.)	Kravitz, Duan, Vohra, and single-patient methodology pioneers
Typ	Experimental single-subject design with adaptive decision rules	Research Design
Ursprungskälla	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.
Alias	Adaptive SSED, Adaptive N-of-1 design, Adaptive single-case experimental design, Adaptive SCE design	single-patient RCT, n=1 trial, individual RCT, crossover n-of-1
Närliggande	4	3
Sammanfattning	Adaptive single-subject experimental design (adaptive SSED) is an experimental methodology in which a single participant or unit is repeatedly observed under systematically alternated conditions — baseline and intervention — while pre-specified decision rules allow the researcher or clinician to modify treatment parameters, phase lengths, or condition sequences in response to continuously collected data. It merges the internal validity of classical single-case experimental designs with the flexibility of adaptive trial logic, making it especially valuable in clinical, behavioral, and applied settings where individual response trajectories vary substantially.	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.