Conducts sensitivity analyses for unmeasured confounding for either an observational study or a metaanalysis of observational studies. For a single observational study, the package reports Evalues, 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 treatmentoutcome association, conditional on the measured covariates. You can use one of the evalues.XX() functions to compute Evalues for the relevant outcome types. Outcome types include risk ratios, odds ratio with common or rare outcomes, hazard ratios with common or rare outcomes, and standardized differences in outcomes. Optionally, you can use the bias_plot() function to plot the bias factor as a function of two sensitivity parameters. (See VanderWeele & Ding, 2017 [
Package details 


Author  Maya B. Mathur, Peng Ding, Tyler J. VanderWeele 
Date of publication  20180918 15:20:02 UTC 
Maintainer  Maya B. Mathur <[email protected]> 
License  GPL2 
Version  1.1.5 
Package repository  View on CRAN 
Installation 
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