| mederrRank-package {mederrRank} | R Documentation |
Bayesian Methods for Identifying the Most Harmful Medication Errors
Description
Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.
Details
The package is loaded with the usual library(mederrRank) command. The most important functions are bhm.mcmc, bhm.resample and mixnegbinom.em.
Author(s)
Sergio Venturini, Jessica Myers
Maintainer: Sergio Venturini <sergio.venturini@unicatt.it>
References
Myers, J. A., Venturini, S., Dominici, F. and Morlock, L. (2011), "Random Effects Models for Identifying the Most Harmful Medication Errors in a Large, Voluntary Reporting Database". Technical Report.
See Also
bayes.rank,
bhm.mcmc,
bhm.resample,
mixnegbinom.em.