方法对比

	Exposome-Wide Association Study	E-Value Sensitivity Analysis
领域	Social Epidemiology	Social Epidemiology
方法族	Regression model	Process / pipeline
起源年份	2010	2017
提出者	Chirag J. Patel, Jayanta Bhattacharya & Atul J. Butte (ExWAS); Christopher P. Wild (exposome concept)	Tyler J. VanderWeele & Peng Ding
类型	Agnostic high-throughput association scan over many environmental exposures	Assumption-free sensitivity analysis for unmeasured confounding
开创性文献	Patel, C. J., Bhattacharya, J., & Butte, A. J. (2010). An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus. PLoS ONE, 5(5), e10746.	VanderWeele, T. J., & Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4), 268-274.
别名	ExWAS, Environment-Wide Association Study, EWAS (environmental), Agnostic Exposure Scan	E-Value, E-Value for Unmeasured Confounding, VanderWeele-Ding E-Value, Bias Factor Sensitivity Analysis
相关	3	3
摘要	An exposome-wide association study (ExWAS), originally introduced as the Environment-Wide Association Study, applies the logic of the genome-wide association study to the environment. Where a GWAS scans hundreds of thousands of genetic variants for association with a trait, an ExWAS scans a broad panel of measured environmental exposures — nutrients, pollutants, chemical biomarkers, infectious markers, and behaviors — against a health outcome, fitting one adjusted regression per exposure and then rigorously controlling the multiple-testing burden across the whole set. The approach was demonstrated by Chirag Patel, Jayanta Bhattacharya, and Atul Butte in 2010 on type 2 diabetes using NHANES data, and it operationalizes Christopher Wild's 2005 concept of the 'exposome': the totality of environmental exposures complementing the genome. ExWAS turns environmental epidemiology from a one-exposure-at-a-time enterprise into a systematic, hypothesis-generating discovery scan.	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.