Comparer des méthodes

	Case-Time-Control Design	Negative Control Outcome Design
Domaine	Social Epidemiology	Social Epidemiology
Famille	Process / pipeline	Process / pipeline
Année d'origine	1995	2010
Auteur d'origine	Samy Suissa; Sander Greenland	Marc Lipsitch, Eric Tchetgen Tchetgen & Ted Cohen; Xu Shi & Wang Miao
Type	Self-controlled observational design with a time-trend control series	Falsification-and-correction pipeline for unmeasured confounding
Source fondatrice	Suissa, S. (1995). The case-time-control design. Epidemiology, 6(3), 248-253.	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.
Alias	Case-Time-Control Method, Trend-Adjusted Case-Crossover, Suissa Case-Time-Control Design, Case-Crossover with Time Controls	Negative Controls, Negative Control Outcome, Negative Control Exposure, Falsification Endpoint Analysis
Apparentées	4	4
Résumé	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 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.