Порівняння методів

	Case-Time-Control Design	Self-Controlled Case Series
Галузь	Social Epidemiology	Social Epidemiology
Родина	Process / pipeline	Process / pipeline
Рік появи	1995	1995
Автор методу	Samy Suissa; Sander Greenland	C. Paddy Farrington
Тип	Self-controlled observational design with a time-trend control series	Within-person case-only design for transient exposures and acute outcomes
Основоположне джерело	Suissa, S. (1995). The case-time-control design. Epidemiology, 6(3), 248-253.	Farrington, C. P. (1995). Relative Incidence Estimation from Case Series for Vaccine Safety Evaluation. Biometrics, 51(1), 228-235.
Інші назви	Case-Time-Control Method, Trend-Adjusted Case-Crossover, Suissa Case-Time-Control Design, Case-Crossover with Time Controls	SCCS, Case Series Method, Within-Person Comparison Design, Farrington Method
Пов'язані	4	3
Підсумок	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.	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.