Allows user to conduct a simulation based quantitative bias analysis using covariate structures generated with individual-level data to characterize the bias arising from unmeasured confounding. Users can specify their desired data generating mechanisms to simulate data and quantitatively summarize findings in an end-to-end application using this package.
Unmeasured confounding is often raised as a source of potential bias when evaluating non-randomized study protocols, but evaluating such concerns during their design remains challenging. We propose a flexible methodology based on individual level simulations that can allow researchers to characterize the bias arising from unmeasured confounding with a specified but modifiable structure during the study design.
sim.BA allows user to conduct a simulation-based quantitative bias
analysis using covariate structures generated with individual-level data
to characterize the bias arising from unmeasured confounding. Users can
specify their desired data generating mechanisms to simulate data and
quantitatively summarize findings in an end-to-end application using
this package. See vignette("sim.BA") for details.
You can install the development version of sim.BA from GitLab with:
# install.packages("remotes")
remotes::install_gitlab("rjd48/sim.BA", host = "gitlab-scm.partners.org")
You can install the published version from CRAN with:
install.packages("sim.BA")