方法对比

	Negative Control Outcome Design	Self-Controlled Case Series
领域	Social Epidemiology	Social Epidemiology
方法族	Process / pipeline	Process / pipeline
起源年份	2010	1995
提出者	Marc Lipsitch, Eric Tchetgen Tchetgen & Ted Cohen; Xu Shi & Wang Miao	C. Paddy Farrington
类型	Falsification-and-correction pipeline for unmeasured confounding	Within-person case-only design for transient exposures and acute outcomes
开创性文献	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.	Farrington, C. P. (1995). Relative Incidence Estimation from Case Series for Vaccine Safety Evaluation. Biometrics, 51(1), 228-235.
别名	Negative Controls, Negative Control Outcome, Negative Control Exposure, Falsification Endpoint Analysis	SCCS, Case Series Method, Within-Person Comparison Design, Farrington Method
相关	4	3
摘要	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.	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.