방법 비교

	Sibling Fixed-Effects Design	Negative Control Outcome Design
분야	Social Epidemiology	Social Epidemiology
계열	Regression model	Process / pipeline
기원 연도	2013	2010
창시자	Brian D'Onofrio, Benjamin Lahey, Eric Turkheimer & Paul Lichtenstein; Thomas Frisell et al.	Marc Lipsitch, Eric Tchetgen Tchetgen & Ted Cohen; Xu Shi & Wang Miao
유형	Within-family fixed-effects design for confounding control	Falsification-and-correction pipeline for unmeasured confounding
원전	Frisell, T., Oberg, S., Kuja-Halkola, R., & Sjolander, A. (2012). Sibling Comparison Designs: Bias From Non-Shared Confounders and Measurement Error. Epidemiology, 23(5), 713-720.	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.
별칭	Sibling Comparison Design, Within-Family Fixed Effects, Discordant Sibling Design, Discordant Twin Design	Negative Controls, Negative Control Outcome, Negative Control Exposure, Falsification Endpoint Analysis
관련	4	4
요약	The sibling fixed-effects, or sibling-comparison, design controls for everything that siblings share by construction. Genes (on average half), parents, household income, neighborhood, schooling, and family culture are differenced out when you compare brothers or sisters who differ in an exposure, so the residual within-family association is purged of all confounders common to the family. D'Onofrio, Lahey, Turkheimer, and Lichtenstein championed these family-based quasi-experiments as a way to integrate genetic and social-science research by rigorously testing competing causal hypotheses. Frisell and colleagues, however, gave the design its essential warning label: precisely because shared confounding is removed, the within-family estimate is unusually vulnerable to the confounders siblings do not share and to attenuation from measurement error. The design is powerful but double-edged.	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.