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	Pilotstudie med enpunktsdesign	Adaptiv enpersonsdesign
Ämnesområde	Försöksplanering	Försöksplanering
Familj	Process / pipeline	Process / pipeline
Ursprungsår	2000s–2010s (as an explicitly named piloting strategy for SSED)	Classical SSED: 1960s–1970s; adaptive extensions formalised: 2000s–2010s
Upphovsperson	Derived from single-subject experimental design traditions (Sidman, 1960; Kazdin) and pilot study methodology (Lancaster, Dodd, Williamson, 2004; Thabane et al., 2010)	Evolved from classical single-case designs (Skinner, Sidman); adaptive features formalised in clinical N-of-1 literature (Zucker, Schmid, Nikles et al.)
Typ	Pilot experimental design	Experimental single-subject design with adaptive decision rules
Ursprungskälla	Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., ... & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.	Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 978-0195341881
Alias	pilot SSED, pilot N-of-1 experiment, pilot single-case experimental design, pilot SCED	Adaptive SSED, Adaptive N-of-1 design, Adaptive single-case experimental design, Adaptive SCE design
Närliggande	6	4
Sammanfattning	A pilot single-subject experimental design (pilot SSED) is a small-scale feasibility trial applied to one or very few individuals, combining the repeated-measurement logic of single-subject experimental design with the explicit preparatory aims of a pilot study. It is used to test an intervention protocol, measurement procedures, and design logistics before committing to a full-scale single-case or group experiment.	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.