Sammenlign metoder

	Case-Time-Control Design	Interrupted Time Series for Public Health
Fagområde	Social Epidemiology	Social Epidemiology
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1995	2002
Ophavsperson	Samy Suissa; Sander Greenland	Anita K. Wagner, Stephen B. Soumerai et al. (segmented-regression formulation); James Lopez Bernal, Steven Cummins & Antonio Gasparrini (public-health tutorial)
Type	Self-controlled observational design with a time-trend control series	Quasi-experimental design estimating level and slope changes in a population outcome after an intervention
Oprindelig kilde	Suissa, S. (1995). The case-time-control design. Epidemiology, 6(3), 248-253.	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.
Aliasser	Case-Time-Control Method, Trend-Adjusted Case-Crossover, Suissa Case-Time-Control Design, Case-Crossover with Time Controls	ITS, Segmented Regression Analysis, Interrupted Time Series Analysis, Quasi-Experimental Time Series Evaluation
Relaterede	4	3
Resumé	The case-time-control design is a pharmacoepidemiologic study design that repairs a specific weakness of the case-crossover study: bias from a secular trend in exposure. In a case-crossover analysis each case acts as their own control, comparing exposure in a short hazard window just before the event to exposure in earlier reference windows, which automatically removes all fixed, time-invariant confounders. But if the prevalence of exposure is rising or falling over calendar time for reasons unrelated to the outcome, this within-person comparison is biased. Samy Suissa's 1995 design adds a separate control series, analyzed the same way, to estimate that pure time trend; dividing the case-crossover odds ratio by the control odds ratio cancels the trend and leaves the exposure effect. Sander Greenland's 1996 analysis clarified the assumptions: the correction works only if the controls share the same exposure trend and there is no within-subject confounder, and it can introduce new bias if those conditions fail.	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.