Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies

2019/01/24 by Sebastien Haneuse, Tyler J. VanderWeele, David Arterburn · 1,012 citations
Economics, Econometrics and Finance · Mathematics · Medicine · #Confounding #Econometrics #Health Systems, Economic Evaluations, Quality of Life #Healthcare Policy and Management #Internal medicine #Liver Disease Diagnosis and Treatment #Mathematics #Medicine #Observational study #Statistics #Value (mathematics)

paper · doi:10.1001/jama.2018.21554

published in JAMA 321(6), 602 (American Medical Association)

openalex publication_date 2019/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

This Guide to Statistics and Methods discusses E-value analysis, an alternative approach to sensitivity analyses for unmeasured confounding in observational studies that specifies the degree of unmeasured confounding that would need to be operative to negate observed results in a study.

Citations

• Sensitivity Analysis in Observational Research: Introducing the E-Value

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• Sensitivity Analysis in Observational Research: Introducing the E-Value
• Contextualizing E-values for Interpretable Sensitivity to Unmeasured Confounding Analyses
• Sensitivity Analysis for Unmeasured Confounding via Effect Extrapolation
• Sensitivity Analysis Without Assumptions
• Hypothesis Testing with E-values