EVALUE: Stata module for conducting sensitivity analyses for unmeasured confounding in observational studies

Ariel Linden, Maya B. Mathur and Tyler J. VanderWeele
Additional contact information
Maya B. Mathur: Department of Epidemiology, Harvard University
Tyler J. VanderWeele: Department of Epidemiology, Harvard University

Statistical Software Components from Boston College Department of Economics

Abstract: evalue performs sensitivity analyses for unmeasured confounding in observational studies using the methodology proposed by VanderWeele and Ding (2017). 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 and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. evalue computes E-values for point estimates (and optionally, confidence limits) for several common outcome types, including risk and rate ratios, odds ratios with common or rare outcomes, hazard ratios with common or rare outcomes, standardized mean differences in outcomes, and risk differences. evalue produces identical results to those computed in the EValue package for R and the web-based graphical interface.

Language: Stata
Requires: Stata version 11
Keywords: sensitivity analysis; causality; confounding; observational studies (search for similar items in EconPapers)
Date: 2019-01-24, Revised 2024-03-06
Note: This module should be installed from within Stata by typing "ssc install evalue". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/e/evalue.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/e/evalue.sthlp help file (text/plain)

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