手法を比較

	Interrupted Time Series for Public Health	Self-Controlled Case Series
分野	Social Epidemiology	Social Epidemiology
系統	Process / pipeline	Process / pipeline
提唱年	2002	1995
提唱者	Anita K. Wagner, Stephen B. Soumerai et al. (segmented-regression formulation); James Lopez Bernal, Steven Cummins & Antonio Gasparrini (public-health tutorial)	C. Paddy Farrington
種類	Quasi-experimental design estimating level and slope changes in a population outcome after an intervention	Within-person case-only design for transient exposures and acute outcomes
原典	Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented Regression Analysis of Interrupted Time Series Studies in Medication Use Research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299-309.	Farrington, C. P. (1995). Relative Incidence Estimation from Case Series for Vaccine Safety Evaluation. Biometrics, 51(1), 228-235.
別名	ITS, Segmented Regression Analysis, Interrupted Time Series Analysis, Quasi-Experimental Time Series Evaluation	SCCS, Case Series Method, Within-Person Comparison Design, Farrington Method
関連	3	3
概要	Interrupted time series analysis, usually implemented as segmented regression, is a strong quasi-experimental design for evaluating the effect of a public-health intervention introduced at a known point in time. By tracking a population-level outcome — prescribing rates, infections, injuries, hospital admissions — over many equally spaced periods before and after the intervention, it asks whether the outcome's level jumped and whether its underlying trend changed when the intervention took effect, relative to the pre-intervention trajectory projected forward as the counterfactual. The segmented-regression formulation was popularized for intervention research by Wagner, Soumerai and colleagues, and Lopez Bernal, Cummins and Gasparrini's 2017 International Journal of Epidemiology tutorial is the standard modern guide for public-health applications, covering autocorrelation, seasonality, and the use of comparison series.	The self-controlled case series, or SCCS, is a case-only study design for estimating the association between a transient exposure and an acute event by comparing each individual's event rate during exposed time windows with their rate during unexposed time windows. Developed by Paddy Farrington in 1995 for vaccine safety evaluation, it uses data only on people who experienced the outcome, and because each person serves as their own control, it automatically eliminates all fixed within-person confounders — genetics, sex, chronic conditions, socioeconomic position — without ever measuring them. A conditional Poisson likelihood removes the individual-level baseline rate and yields a relative incidence comparing risk to control periods. Whitaker, Farrington, Spiessens and Musonda's 2006 Statistics in Medicine tutorial is the standard practical guide to fitting and interpreting the model.