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 [<http://annals.org/aim/article/2643434>] for details.) For a metaanalysis, use the function confounded_meta to compute point estimates and inference for: (1) the proportion of studies with true causal effect sizes more extreme than a specified threshold of scientific importance; and (2) the minimum bias factor and confounding strength required to reduce to less than a specified threshold the proportion of studies with true effect sizes of scientifically significant size. The functions sens_plot() and sens_table() create plots and tables for visualizing these metaanalysis metrics across a range of bias values, and scrape_meta helps scrape studylevel data from a published forest plot or summary table to obtain the needed estimates when these are not reported. (See Mathur & VanderWeele [<https://osf.io/jkhfg/>] for details.) Most of the analyses available in this package can also be conducted using webbased graphical interfaces (for a single observational study: [<https://evalue.hmdc.harvard.edu/app/>]; for a metaanalysis: [<https://mmathur.shinyapps.io/meta_gui_2/>]).
Package details 


Author  Maya B. Mathur, Peng Ding, Tyler J. VanderWeele 
Maintainer  Maya B. Mathur <[email protected]> 
License  GPL2 
Version  1.1.5 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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