Порівняння методів

	Self-Controlled Case Series	Negative Control Outcome Design
Галузь	Social Epidemiology	Social Epidemiology
Родина	Process / pipeline	Process / pipeline
Рік появи	1995	2010
Автор методу	C. Paddy Farrington	Marc Lipsitch, Eric Tchetgen Tchetgen & Ted Cohen; Xu Shi & Wang Miao
Тип	Within-person case-only design for transient exposures and acute outcomes	Falsification-and-correction pipeline for unmeasured confounding
Основоположне джерело	Farrington, C. P. (1995). Relative Incidence Estimation from Case Series for Vaccine Safety Evaluation. Biometrics, 51(1), 228-235.	Lipsitch, M., Tchetgen Tchetgen, E., & Cohen, T. (2010). Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies. Epidemiology, 21(3), 383-388.
Інші назви	SCCS, Case Series Method, Within-Person Comparison Design, Farrington Method	Negative Controls, Negative Control Outcome, Negative Control Exposure, Falsification Endpoint Analysis
Пов'язані	3	4
Підсумок	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.	The negative control design uses a deliberately chosen outcome (or exposure) that cannot plausibly be caused by the exposure under study, yet is subject to the same unmeasured confounding, selection, or measurement processes as the real research question. If the exposure appears to 'affect' something it cannot possibly affect, that spurious association is a signature of residual bias. Lipsitch, Tchetgen Tchetgen, and Cohen formalized this falsification logic for epidemiology in 2010, specifying the conditions a valid negative control must satisfy. Shi, Miao, and Tchetgen Tchetgen's 2020 review extended the idea from detection toward correction, showing how pairs of negative control variables underpin proximal causal inference, which can recover an unbiased effect estimate even when the confounder is never measured.