Salīdzināt metodes

	Four-Way Decomposition	E-Value Sensitivity Analysis
Nozare	Social Epidemiology	Social Epidemiology
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2014	2017
Autors	Tyler J. VanderWeele	Tyler J. VanderWeele & Peng Ding
Tips	Counterfactual decomposition pipeline for total effects	Assumption-free sensitivity analysis for unmeasured confounding
Pirmavots	VanderWeele, T. J. (2014). A unification of mediation and interaction: a four-way decomposition. Epidemiology, 25(5), 749-761.	VanderWeele, T. J., & Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4), 268-274.
Citi nosaukumi	4-Way Decomposition, VanderWeele Four-Way Decomposition, Mediation-Interaction Decomposition, Unification of Mediation and Interaction	E-Value, E-Value for Unmeasured Confounding, VanderWeele-Ding E-Value, Bias Factor Sensitivity Analysis
Saistītās	3	3
Kopsavilkums	The four-way decomposition, introduced by Tyler VanderWeele in 2014, unifies the two great themes of effect analysis — mediation and interaction — into a single, exhaustive partition of a total causal effect. Any total effect of an exposure on an outcome can be split into exactly four pieces: a controlled direct effect (neither mediation nor interaction), a reference interaction (interaction but no mediation), a mediated interaction (both mediation and interaction at once), and a pure indirect effect (mediation but no interaction). These four components are mutually exclusive and add up to the total effect, and they nest the familiar two-way and three-way decompositions as special cases. Formalized in counterfactual notation and developed at book length in VanderWeele's 2015 Explanation in Causal Inference, the method gives social epidemiologists a precise vocabulary for asking how much of an exposure's effect runs through a mediator, how much depends on the exposure and mediator acting together, and how much is direct.	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.