Compară metode

	Design Experimental Adaptiv Monosujet	Analiza seriilor de timp întrerupte (ITS)
Domeniu	Design experimental	Inferență cauzală
Familie	Process / pipeline	Regression model
Anul apariției	Classical SSED: 1960s–1970s; adaptive extensions formalised: 2000s–2010s	2002
Autorul original	Evolved from classical single-case designs (Skinner, Sidman); adaptive features formalised in clinical N-of-1 literature (Zucker, Schmid, Nikles et al.)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Tip	Experimental single-subject design with adaptive decision rules	Quasi-experimental segmented regression
Sursa seminală	Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 978-0195341881	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
Denumiri alternative	Adaptive SSED, Adaptive N-of-1 design, Adaptive single-case experimental design, Adaptive SCE design	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Înrudite	4	5
Rezumat	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.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.