logp

This function computes the negative log-posterior distribution of the Bayesian hierarchical model described in Myers et al (2011). It is a helper function and not meant to be used on its own.

Medication Errors

Optimal Bayesian Ranking

ranking

ranks

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.

Sergio Venturini

mederrRank

Bayesian Methods for Identifying the Most Harmful Medication
Errors

logp function

<dl> <dt>theta</dt>
<dd>value of the error profile random effect at which the log.posterior distribution is calculated.</dd> <dt>deltaj</dt>
<dd>vector of hospital random effect values.</dd> <dt>sigma2</dt>
<dd>scale parameter (\(&gt; 0\)).</dd> <dt>i</dt>
<dd>error profile index for which the calculate of the log.posterior distribution is needed.</dd> <dt>k</dt>
<dd>degrees of freedom (\(&gt; 0\), maybe non-integer). <code>df = Inf</code> is allowed.</dd> <dt>eta</dt>
<dd>skewness parameter (\(&gt; 0\)).</dd> <dt>dat</dt>
<dd>an object of <a href="/link/class?package=mederrRank&version=0.1.0" data-mini-rdoc="mederrRank::class">class</a> "mederrData".</dd></dl>

Arguments

Sergio Venturini <a href="/link/sergio.venturini%40unicatt.it?package=mederrRank&version=0.1.0" data-mini-rdoc="mederrRank::sergio.venturini@unicatt.it">sergio.venturini@unicatt.it</a>,
Jessica A. Myers <a href="/link/jmyers6%40partners.org?package=mederrRank&version=0.1.0" data-mini-rdoc="mederrRank::jmyers6@partners.org">jmyers6@partners.org</a>

Author

Negative Log-Posterior Function of the Bayesian Hierarchical Model for Identifying the Most Harmful Medication Errors — logp

<dl>

 <dt>theta</dt>
<dd>value of the error profile random effect at which the log.posterior distribution is calculated.</dd>

 <dt>deltaj</dt>
<dd>vector of hospital random effect values.</dd>

 <dt>sigma2</dt>
<dd>scale parameter (\(&gt; 0\)).</dd>

 <dt>i</dt>
<dd>error profile index for which the calculate of the log.posterior distribution is needed.</dd>

 <dt>k</dt>
<dd>degrees of freedom (\(&gt; 0\), maybe non-integer). <code>df = Inf</code> is allowed.</dd>

 <dt>eta</dt>
<dd>skewness parameter (\(&gt; 0\)).</dd>

 <dt>dat</dt>
<dd>an object of <a href='https://rdrr.io/r/base/class.html'>class</a> "mederrData".</dd>

</dl>

Sergio Venturini <a href='mailto:sergio.venturini@unicatt.it'>sergio.venturini@unicatt.it</a>,
Jessica A. Myers <a href='mailto:jmyers6@partners.org'>jmyers6@partners.org</a>

logp: Negative Log-Posterior Function of the Bayesian Hierarchical Model for Identifying the Most Harmful Medication Errors

Description

Usage

Value

Arguments

Author

Details

References

See Also