手法を比較

	適応的単一被験者実験計画	N-of-1試験
分野	実験計画法	臨床研究
系統	Process / pipeline	Process / pipeline
提唱年	Classical SSED: 1960s–1970s; adaptive extensions formalised: 2000s–2010s	1990s-2010s
提唱者	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
種類	Experimental single-subject design with adaptive decision rules	Research Design
原典	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.
別名	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
関連	4	3
概要	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.