Conducting sensitivity analysis for unmeasured confounding in observational studies using E-values: The evalue package

Ariel Linden, Maya B. Mathur () and Tyler J. VanderWeele ()
Additional contact information
Maya B. Mathur: Harvard University
Tyler J. VanderWeele: tvanderw@hsph.harvard.edu

Stata Journal, 2020, vol. 20, issue 1, 162-175

Abstract: In this article, we introduce the evalue package, which performs sensitivity analyses for unmeasured confounding in observational studies using the methodology proposed by VanderWeele and Ding (2017, Annals of Inter- nal Medicine 167: 268–274). evalue reports E-values, defined as the minimum strength of association on the risk-ratio scale that an unmeasured confounder would need to have with both the treatment assignment and the outcome to fully explain away a specific treatment-outcome association, conditional on the mea- sured covariates. evalue computes E-values for point estimates (and optionally, confidence limits) for several common outcome types, including risk and rate ra- tios, odds ratios with common or rare outcomes, hazard ratios with common or rare outcomes, standardized mean differences in outcomes, and risk differences.

Keywords: evalue; E-value; sensitivity analysis; treatment effects; causality; confounding (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1177/1536867X20909696

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