방법 비교

	E-Value Sensitivity Analysis	Parametric g-Formula
분야	Social Epidemiology	Social Epidemiology
계열	Process / pipeline	Process / pipeline
기원 연도	2017	1986
창시자	Tyler J. VanderWeele & Peng Ding	James M. Robins; Ashley I. Naimi, Alexander P. Keil et al. (applied tutorial)
유형	Assumption-free sensitivity analysis for unmeasured confounding	Counterfactual simulation pipeline for time-varying treatment regimes
원전	VanderWeele, T. J., & Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4), 268-274.	Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.
별칭	E-Value, E-Value for Unmeasured Confounding, VanderWeele-Ding E-Value, Bias Factor Sensitivity Analysis	g-Computation Formula, Robins' g-Formula, Parametric g-Computation, Generalized Computation Algorithm Formula
관련	3	3
요약	The E-value, introduced by Tyler VanderWeele and Peng Ding in 2017, is a simple, assumption-free way to quantify how robust an observational association is to unmeasured confounding. It answers a single, sharply posed question: how strong would an unmeasured confounder have to be — in its association with both the exposure and the outcome — to fully explain away the observed effect? The larger the E-value, the more powerful a hidden confounder would need to be, and so the more robust the finding. The method rests on the bounding factor derived by Ding and VanderWeele in their 2016 'Sensitivity analysis without assumptions,' which holds regardless of the distribution or number of unmeasured confounders. Because it requires only the point estimate and confidence limit on the risk-ratio scale and no untestable bias parameters, the E-value has become a routine reporting standard in observational epidemiology, including social epidemiology where unmeasured confounding is pervasive.	The parametric g-formula is the estimator James Robins introduced in 1986 to recover the causal effect of a time-varying exposure when time-varying confounders are themselves affected by past exposure — a setting where standard regression adjustment is guaranteed to give the wrong answer. Rather than conditioning on the troublesome confounders directly, the g-formula reconstructs the entire counterfactual world: it parametrically estimates how confounders and the outcome evolve over time, then Monte-Carlo simulates what would have happened to the population under a hypothetical exposure regime such as 'always exposed' versus 'never exposed.' Keil and colleagues' 2014 worked tutorial for time-to-event data made the algorithm concrete for epidemiologists. In social epidemiology it is the workhorse for questions like the cumulative effect of sustained neighborhood deprivation, employment, or income trajectories on health, where mediators and confounders are tangled across time.