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 · 10 citations
Medicine · Economics, Econometrics and Finance · #Liver Disease Diagnosis and Treatment #Healthcare Policy and Management #Health Systems, Economic Evaluations, Quality of Life

paper · doi:10.1001/jama.2018.21554

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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Related

• 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