# Welcome to Simplified Bayesian Sensitivity Analysis project!

SBSA package implements simplified Bayesian sensitivity analysis of models with partially
observed confounders.

SBSA is an R package that offers a simplified interface to Bayesian
sensitivity analysis. It allows a formal but natural approach to
investigation of the nature of poorly measured and unobserved
confounders in collected data.

Consider a study investigating the association between an outcome
and a set of exposure variables. Some of the variables are
confounders, sometimes poorly measured. Other potential confounders
may have gone unmeasured. Therefore, simply using regression on this
data brings a while range of potential problems.

Sensitivity
analysis can be used to asses how these limitations in available
data impact inference. In practice, however, sensitivity analysis
can be challenging to perform, or to interpret
results. *Simplified Bayesian sensitivity analysis*, proposed
by Gustafson *et al.*[1], aims to
find a blance between realism and simplicity in conducting
sensitivity analysis. It provides a model of data that requires the
user to provide only a few hyperparameters, and a computational
method to effectively apply MCMC to estimate the model.

This project implements Simplified Bayesian sensitivity analysis for
both continuous and binary variables, as described by
Gustafson *et al.*. The package can be installed
from CRAN.
To view the source repository and other project details, please
visit the project summary page on R-Forge.

## References

- Gustafson, P., McCandless, L.C., Levy,
A.R., and Richardson, S. (2010). "Simplified Bayesian sensitivity
analysis for mismeasured and unobserved confounders." Biometrics 66,
1129-1137.
(DOI: 10.1111/j.1541-0420.2009.01377.x)