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(v2.1.1.9133) update math formulae

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dr. M.S. (Matthijs) Berends committed 2025-01-27 22:43:35 +01:00
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commit 2561494e06
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.github/workflows/check-current-testthat.yaml
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@@ -63,10 +63,10 @@ jobs:
- {os: ubuntu-latest, r: 'release', allowfail: false}
# older versions (see also check-old-tinytest.yaml for even older versions):
- {os: ubuntu-latest, r: 'oldrel', allowfail: false}
- {os: ubuntu-latest, r: 'oldrel-1', allowfail: false}
- {os: ubuntu-latest, r: 'oldrel-2', allowfail: false}
- {os: ubuntu-latest, r: 'oldrel-3', allowfail: false}
- {os: ubuntu-latest, r: 'oldrel-4', allowfail: false}
env:
GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
PythonPackage/AMR/AMR/datasets.py
-1
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@@ -21,7 +21,6 @@ utils = importr('utils')
base.options(warn = -1)
# Override R library paths globally for the session
robjects.r(f'.Library <- "{r_lib_path}"') # Replace default library
robjects.r(f'.Library.site <- "{r_lib_path}"') # Replace site-specific library
base._libPaths(r_lib_path) # Override .libPaths() as well
PythonPackage/AMR/dist/AMR-2.1.1.9133-py3-none-any.whl
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PythonPackage/AMR/dist/amr-2.1.1.9133.tar.gz
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R/antibiogram.R
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@@ -195,48 +195,26 @@
#' You can also use functions from specific 'table reporting' packages to transform the output of [antibiogram()] to your needs, e.g. with `flextable::as_flextable()` or `gt::gt()`.
#'
#' @section Why Use WISCA?:
#' WISCA, as outlined by Barbieri *et al.* (\doi{10.1186/s13756-021-00939-2}), stands for
#' Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability
#' of adequate empirical antimicrobial regimen coverage for specific infection syndromes.
#' This method leverages a Bayesian hierarchical logistic regression framework with random
#' effects for pathogens and regimens, enabling robust estimates in the presence of sparse
#' data.
#'
#' The Bayesian model assumes conjugate priors for parameter estimation. For example, the
#' coverage probability \ifelse{latex}{\deqn{$theta$}}{$theta$} for a given antimicrobial regimen
#' is modeled using a Beta distribution as a prior:
#' WISCA, as outlined by Barbieri *et al.* (\doi{10.1186/s13756-021-00939-2}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data.
#'
#' \ifelse{latex}{\deqn{$theta$ \sim \text{Beta}($alpha$_0, $beta$_0)}}{
#' \ifelse{html}{\figure{beta_prior.png}{options: width="300" alt="Beta prior"}}{$theta$ ~ Beta($alpha$_0, $beta$_0)}}
#' The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
#'
#' where \eqn{$alpha$_0} and \eqn{$beta$_0} represent prior successes and failures, respectively,
#' informed by expert knowledge or weakly informative priors (e.g., \eqn{$alpha$_0 = 1, $beta$_0 = 1}).
#' \deqn{\theta \sim \text{Beta}(\alpha_0, \beta_0)}
#'
#' The likelihood function is constructed based on observed data, where the number of covered
#' cases for a regimen follows a binomial distribution:
#' where \eqn{\alpha_0} and \eqn{\beta_0} represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \eqn{\alpha_0 = 1, \beta_0 = 1}). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:
#'
#' \ifelse{latex}{\deqn{y \sim \text{Binomial}(n, $theta$)}}{
#' \ifelse{html}{\figure{binomial_likelihood.png}{options: width="300" alt="Binomial likelihood"}}{y ~ Binomial(n, $theta$)}}
#' \deqn{y \sim \text{Binomial}(n, \theta)}
#'
#' Posterior parameter estimates are obtained by combining the prior and likelihood using
#' Bayes' theorem. The posterior distribution of \eqn{$theta$} is also a Beta distribution:
#' Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \eqn{\theta} is also a Beta distribution:
#'
#' \ifelse{latex}{\deqn{$theta$ | y \sim \text{Beta}($alpha$_0 + y, $beta$_0 + n - y)}}{
#' \ifelse{html}{\figure{posterior_beta.png}{options: width="300" alt="Beta posterior"}}{$theta$ | y ~ Beta($alpha$_0 + y, $beta$_0 + n - y)}}
#' \deqn{\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)}
#'
#' For hierarchical modeling, pathogen-level effects (e.g., differences in resistance
#' patterns) and regimen-level effects are modelled using Gaussian priors on log-odds.
#' This hierarchical structure ensures partial pooling of estimates across groups,
#' improving stability in strata with small sample sizes. The model is implemented using
#' Hamiltonian Monte Carlo (HMC) sampling.
#' For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
#'
#' Stratified results are provided based on covariates such as age, sex, and clinical
#' complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities).
#' For example, posterior odds ratios (ORs) are derived to quantify the effect of these
#' covariates on coverage probabilities:
#' Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using `dplyr`'s [group_by()] as a pre-processing step before running [wisca()]. In this case, posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
#'
#' \ifelse{latex}{\deqn{\text{OR}_{\text{covariate}} = \frac{\exp($beta$_{\text{covariate}})}{\exp($beta$_0)}}}{
#' \ifelse{html}{\figure{odds_ratio.png}{options: width="300" alt="Odds ratio formula"}}{OR_covariate = exp(beta_covariate) / exp(beta_0)}}
#' \deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}}
#'
#' By combining empirical data with prior knowledge, WISCA overcomes the limitations
#' of traditional combination antibiograms, offering disease-specific, patient-stratified
@@ -249,6 +227,7 @@
#' * Klinker KP *et al.* (2021). **Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms**. *Therapeutic Advances in Infectious Disease*, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
#' * Barbieri E *et al.* (2021). **Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach** *Antimicrobial Resistance & Infection Control* May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
#' * **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition**, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' @author Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
#' @rdname antibiogram
#' @name antibiogram
#' @export
@@ -331,7 +310,8 @@
#'
#' ureido <- antibiogram(example_isolates,
#' antibiotics = ureidopenicillins(),
#' ab_transform = "name"
#' ab_transform = "name",
#' wisca = TRUE
#' )
#'
#' # in an Rmd file, you would just need to return `ureido` in a chunk,
R/mo_matching_score.R
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@@ -38,9 +38,7 @@
#' @section Matching Score for Microorganisms:
#' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m}, is calculated as:
#'
#' \ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
#'
#' \ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
#' \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
#'
#' where:
#'
_pkgdown.yml
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@@ -32,6 +32,11 @@ url: "https://msberends.github.io/AMR/"
template:
bootstrap: 5
includes: # support for mathematical formulas, from https://github.com/r-lib/pkgdown/issues/2704#issuecomment-2307055568
in_header: |
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous">
<script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script>
<script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script>
bootswatch: "flatly"
assets: "pkgdown/logos" # use logos in this folder
bslib:
data-raw/_generate_python_wrapper.sh
-1
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@@ -65,7 +65,6 @@ utils = importr('utils')
base.options(warn = -1)
# Override R library paths globally for the session
robjects.r(f'.Library <- "{r_lib_path}"') # Replace default library
robjects.r(f'.Library.site <- "{r_lib_path}"') # Replace site-specific library
base._libPaths(r_lib_path) # Override .libPaths() as well
data-raw/antibiograms.Rmd
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@@ -1,54 +0,0 @@
---
title: "Generating antibiograms with the AMR package"
author: "AMR package developers"
date: "`r Sys.Date()`"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE)
library(AMR)
```
This is an example R Markdown file to show the use of `antibiogram()` of the AMR package.
For starters, this is what our `example_isolates` data set looks like:
```{r}
example_isolates
```
### Traditional Antibiogram
```{r trad}
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()))
```
### Combined Antibiogram
```{r comb}
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
```
### Syndromic Antibiogram
```{r synd}
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward")
```
### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
```{r wisca}
antibiogram(example_isolates,
antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
minimum = 10, # this should be >= 30, but now just as example
syndromic_group = ifelse(example_isolates$age >= 65 &
example_isolates$gender == "M",
"WISCA Group 1", "WISCA Group 2"))
```
data-raw/antibiograms.html
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@@ -1,850 +0,0 @@
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="author" content="AMR package developers" />
<meta name="date" content="2023-02-24" />
<title>Generating antibiograms with the AMR package</title>
<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
});
</script>
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<h1 class="title toc-ignore">Generating antibiograms with the AMR
package</h1>
<h4 class="author">AMR package developers</h4>
<h4 class="date">2023-02-24</h4>
</div>
<p>This is an example R Markdown file to show the use of
<code>antibiogram()</code> of the AMR package.</p>
<p>For starters, this is what our <code>example_isolates</code> data set
looks like:</p>
<pre class="r"><code>example_isolates</code></pre>
<pre><code>## # A tibble: 2,000 × 46
## date patient age gender ward mo PEN OXA FLC AMX
## &lt;date&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;chr&gt; &lt;mo&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt;
## 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
## 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
## 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
## 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
## 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
## 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
## 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
## 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
## 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA
## 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA
## # … with 1,990 more rows, and 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;,
## # TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;, CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;,
## # CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;, TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;,
## # TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;, FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;,
## # MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;, TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;,
## # ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;, IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;,
## # CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, RIF &lt;sir&gt;</code></pre>
<div id="traditional-antibiogram" class="section level3">
<h3>Traditional Antibiogram</h3>
<pre class="r"><code>antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()))</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">Pathogen (N min-max)</th>
<th align="right">AMK</th>
<th align="right">GEN</th>
<th align="right">IPM</th>
<th align="right">KAN</th>
<th align="right">MEM</th>
<th align="right">TOB</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">CoNS (43-309)</td>
<td align="right">0</td>
<td align="right">86</td>
<td align="right">52</td>
<td align="right">0</td>
<td align="right">52</td>
<td align="right">22</td>
</tr>
<tr class="even">
<td align="left"><em>E. coli</em> (0-462)</td>
<td align="right">100</td>
<td align="right">98</td>
<td align="right">100</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">97</td>
</tr>
<tr class="odd">
<td align="left"><em>E. faecalis</em> (0-39)</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right">100</td>
<td align="right">0</td>
<td align="right"></td>
<td align="right">0</td>
</tr>
<tr class="even">
<td align="left"><em>K. pneumoniae</em> (0-58)</td>
<td align="right"></td>
<td align="right">90</td>
<td align="right">100</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">90</td>
</tr>
<tr class="odd">
<td align="left"><em>P. aeruginosa</em> (17-30)</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right"></td>
<td align="right">0</td>
<td align="right"></td>
<td align="right">100</td>
</tr>
<tr class="even">
<td align="left"><em>P. mirabilis</em> (0-34)</td>
<td align="right"></td>
<td align="right">94</td>
<td align="right">94</td>
<td align="right"></td>
<td align="right"></td>
<td align="right">94</td>
</tr>
<tr class="odd">
<td align="left"><em>S. aureus</em> (2-233)</td>
<td align="right"></td>
<td align="right">99</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right">98</td>
</tr>
<tr class="even">
<td align="left"><em>S. epidermidis</em> (8-163)</td>
<td align="right">0</td>
<td align="right">79</td>
<td align="right"></td>
<td align="right">0</td>
<td align="right"></td>
<td align="right">51</td>
</tr>
<tr class="odd">
<td align="left"><em>S. hominis</em> (3-80)</td>
<td align="right"></td>
<td align="right">92</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right">85</td>
</tr>
<tr class="even">
<td align="left"><em>S. pneumoniae</em> (11-117)</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right"></td>
<td align="right">0</td>
<td align="right"></td>
<td align="right">0</td>
</tr>
</tbody>
</table>
</div>
<div id="combined-antibiogram" class="section level3">
<h3>Combined Antibiogram</h3>
<pre class="r"><code>antibiogram(example_isolates,
antibiotics = c(&quot;TZP&quot;, &quot;TZP+TOB&quot;, &quot;TZP+GEN&quot;))</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">Pathogen (N min-max)</th>
<th align="right">TZP</th>
<th align="right">TZP + GEN</th>
<th align="right">TZP + TOB</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">CoNS (29-274)</td>
<td align="right">30</td>
<td align="right">97</td>
<td align="right"></td>
</tr>
<tr class="even">
<td align="left"><em>E. coli</em> (416-461)</td>
<td align="right">94</td>
<td align="right">100</td>
<td align="right">99</td>
</tr>
<tr class="odd">
<td align="left"><em>K. pneumoniae</em> (53-58)</td>
<td align="right">89</td>
<td align="right">93</td>
<td align="right">93</td>
</tr>
<tr class="even">
<td align="left"><em>P. aeruginosa</em> (27-30)</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">100</td>
</tr>
<tr class="odd">
<td align="left"><em>P. mirabilis</em> (27-34)</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">100</td>
</tr>
<tr class="even">
<td align="left"><em>S. aureus</em> (7-231)</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">100</td>
</tr>
<tr class="odd">
<td align="left"><em>S. epidermidis</em> (5-128)</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">100</td>
</tr>
<tr class="even">
<td align="left"><em>S. hominis</em> (0-74)</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">100</td>
</tr>
<tr class="odd">
<td align="left"><em>S. pneumoniae</em> (112-112)</td>
<td align="right">100</td>
<td align="right">100</td>
<td align="right">100</td>
</tr>
</tbody>
</table>
</div>
<div id="syndromic-antibiogram" class="section level3">
<h3>Syndromic Antibiogram</h3>
<pre class="r"><code>antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = &quot;ward&quot;)</code></pre>
<table>
<colgroup>
<col width="25%" />
<col width="37%" />
<col width="6%" />
<col width="6%" />
<col width="6%" />
<col width="6%" />
<col width="6%" />
<col width="6%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Syndromic Group</th>
<th align="left">Pathogen (N min-max)</th>
<th align="right">AMK</th>
<th align="right">GEN</th>
<th align="right">IPM</th>
<th align="right">KAN</th>
<th align="right">MEM</th>
<th align="right">TOB</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Clinical</td>
<td align="left">CoNS (23-205)</td>
<td align="right"></td>
<td align="right">89</td>
<td align="right">57</td>
<td align="right"></td>
<td align="right">57</td>
<td align="right">26</td>
</tr>
<tr class="even">
<td align="left">ICU</td>
<td align="left">CoNS (10-73)</td>
<td align="right"></td>
<td align="right">79</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
</tr>
<tr class="odd">
<td align="left">Outpatient</td>
<td align="left">CoNS (3-31)</td>
<td align="right"></td>
<td align="right">84</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left"><em>E. coli</em> (0-299)</td>
<td align="right">100</td>
<td align="right">98</td>
<td align="right">100</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">98</td>
</tr>
<tr class="odd">
<td align="left">ICU</td>
<td align="left"><em>E. coli</em> (0-137)</td>
<td align="right">100</td>
<td align="right">99</td>
<td align="right">100</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">96</td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left"><em>K. pneumoniae</em> (0-51)</td>
<td align="right"></td>
<td align="right">92</td>
<td align="right">100</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right">92</td>
</tr>
<tr class="odd">
<td align="left">Clinical</td>
<td align="left"><em>P. mirabilis</em> (0-30)</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right">100</td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left"><em>S. aureus</em> (2-150)</td>
<td align="right"></td>
<td align="right">99</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right">97</td>
</tr>
<tr class="odd">
<td align="left">ICU</td>
<td align="left"><em>S. aureus</em> (0-66)</td>
<td align="right"></td>
<td align="right">100</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left"><em>S. epidermidis</em> (4-79)</td>
<td align="right"></td>
<td align="right">82</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right">55</td>
</tr>
<tr class="odd">
<td align="left">ICU</td>
<td align="left"><em>S. epidermidis</em> (4-75)</td>
<td align="right"></td>
<td align="right">72</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right">41</td>
</tr>
<tr class="even">
<td align="left">Clinical</td>
<td align="left"><em>S. hominis</em> (1-45)</td>
<td align="right"></td>
<td align="right">96</td>
<td align="right"></td>
<td align="right"></td>
<td align="right"></td>
<td align="right">94</td>
</tr>
<tr class="odd">
<td align="left">Clinical</td>
<td align="left"><em>S. pneumoniae</em> (5-78)</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right"></td>
<td align="right">0</td>
<td align="right"></td>
<td align="right">0</td>
</tr>
<tr class="even">
<td align="left">ICU</td>
<td align="left"><em>S. pneumoniae</em> (5-30)</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="right"></td>
<td align="right">0</td>
<td align="right"></td>
<td align="right">0</td>
</tr>
</tbody>
</table>
</div>
<div id="weighted-incidence-syndromic-combination-antibiogram-wisca" class="section level3">
<h3>Weighted-Incidence Syndromic Combination Antibiogram (WISCA)</h3>
<pre class="r"><code>antibiogram(example_isolates,
antibiotics = c(&quot;AMC&quot;, &quot;AMC+CIP&quot;, &quot;TZP&quot;, &quot;TZP+TOB&quot;),
mo_transform = &quot;gramstain&quot;,
minimum = 10, # this should be &gt;= 30, but now just as example
syndromic_group = ifelse(example_isolates$age &gt;= 65 &amp;
example_isolates$gender == &quot;M&quot;,
&quot;WISCA Group 1&quot;, &quot;WISCA Group 2&quot;))</code></pre>
<table>
<colgroup>
<col width="23%" />
<col width="35%" />
<col width="5%" />
<col width="14%" />
<col width="5%" />
<col width="14%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">Syndromic Group</th>
<th align="left">Pathogen (N min-max)</th>
<th align="right">AMC</th>
<th align="right">AMC + CIP</th>
<th align="right">TZP</th>
<th align="right">TZP + TOB</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">WISCA Group 1</td>
<td align="left">Gram-negative (261-285)</td>
<td align="right">76</td>
<td align="right">95</td>
<td align="right">89</td>
<td align="right">99</td>
</tr>
<tr class="even">
<td align="left">WISCA Group 2</td>
<td align="left">Gram-negative (380-442)</td>
<td align="right">76</td>
<td align="right">98</td>
<td align="right">88</td>
<td align="right">98</td>
</tr>
<tr class="odd">
<td align="left">WISCA Group 1</td>
<td align="left">Gram-positive (123-406)</td>
<td align="right">76</td>
<td align="right">89</td>
<td align="right">81</td>
<td align="right">95</td>
</tr>
<tr class="even">
<td align="left">WISCA Group 2</td>
<td align="left">Gram-positive (222-732)</td>
<td align="right">76</td>
<td align="right">89</td>
<td align="right">88</td>
<td align="right">95</td>
</tr>
</tbody>
</table>
</div>
</div>
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data-raw/antibiograms.pdf
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data-raw/gpt_training_text_v2.1.1.9133.txt
+17 -39
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@@ -1854,48 +1854,26 @@ You can also use functions from specific 'table reporting' packages to transform
}
\section{Why Use WISCA?}{
WISCA, as outlined by Barbieri \emph{et al.} (\doi{10.1186/s13756-021-00939-2}), stands for
Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability
of adequate empirical antimicrobial regimen coverage for specific infection syndromes.
This method leverages a Bayesian hierarchical logistic regression framework with random
effects for pathogens and regimens, enabling robust estimates in the presence of sparse
data.
The Bayesian model assumes conjugate priors for parameter estimation. For example, the
coverage probability \ifelse{latex}{\deqn{$theta$}}{$theta$} for a given antimicrobial regimen
is modeled using a Beta distribution as a prior:
WISCA, as outlined by Barbieri \emph{et al.} (\doi{10.1186/s13756-021-00939-2}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data.
\ifelse{latex}{\deqn{$theta$ \sim \text{Beta}($alpha$_0, $beta$_0)}}{
\ifelse{html}{\figure{beta_prior.png}{options: width="300" alt="Beta prior"}}{$theta$ ~ Beta($alpha$_0, $beta$_0)}}
The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
where \eqn{$alpha$_0} and \eqn{$beta$_0} represent prior successes and failures, respectively,
informed by expert knowledge or weakly informative priors (e.g., \eqn{$alpha$_0 = 1, $beta$_0 = 1}).
\deqn{\theta \sim \text{Beta}(\alpha_0, \beta_0)}
The likelihood function is constructed based on observed data, where the number of covered
cases for a regimen follows a binomial distribution:
where \eqn{\alpha_0} and \eqn{\beta_0} represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \eqn{\alpha_0 = 1, \beta_0 = 1}). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:
\ifelse{latex}{\deqn{y \sim \text{Binomial}(n, $theta$)}}{
\ifelse{html}{\figure{binomial_likelihood.png}{options: width="300" alt="Binomial likelihood"}}{y ~ Binomial(n, $theta$)}}
\deqn{y \sim \text{Binomial}(n, \theta)}
Posterior parameter estimates are obtained by combining the prior and likelihood using
Bayes' theorem. The posterior distribution of \eqn{$theta$} is also a Beta distribution:
Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \eqn{\theta} is also a Beta distribution:
\ifelse{latex}{\deqn{$theta$ | y \sim \text{Beta}($alpha$_0 + y, $beta$_0 + n - y)}}{
\ifelse{html}{\figure{posterior_beta.png}{options: width="300" alt="Beta posterior"}}{$theta$ | y ~ Beta($alpha$_0 + y, $beta$_0 + n - y)}}
\deqn{\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)}
For hierarchical modeling, pathogen-level effects (e.g., differences in resistance
patterns) and regimen-level effects are modelled using Gaussian priors on log-odds.
This hierarchical structure ensures partial pooling of estimates across groups,
improving stability in strata with small sample sizes. The model is implemented using
Hamiltonian Monte Carlo (HMC) sampling.
For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
Stratified results are provided based on covariates such as age, sex, and clinical
complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities).
For example, posterior odds ratios (ORs) are derived to quantify the effect of these
covariates on coverage probabilities:
Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using \code{dplyr}'s \code{\link[=group_by]{group_by()}} as a pre-processing step before running \code{\link[=wisca]{wisca()}}. In this case, posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
\ifelse{latex}{\deqn{\text{OR}_{\text{covariate}} = \frac{\exp($beta$_{\text{covariate}})}{\exp($beta$_0)}}}{
\ifelse{html}{\figure{odds_ratio.png}{options: width="300" alt="Odds ratio formula"}}{OR_covariate = exp(beta_covariate) / exp(beta_0)}}
\deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}}
By combining empirical data with prior knowledge, WISCA overcomes the limitations
of traditional combination antibiograms, offering disease-specific, patient-stratified
@@ -1982,7 +1960,8 @@ antibiogram(example_isolates,
ureido <- antibiogram(example_isolates,
antibiotics = ureidopenicillins(),
ab_transform = "name"
ab_transform = "name",
wisca = TRUE
)
# in an Rmd file, you would just need to return `ureido` in a chunk,
@@ -2016,6 +1995,9 @@ plot(ab1)
plot(ab2)
}
}
\author{
Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
}
@@ -3099,9 +3081,7 @@ This is based on:
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, is calculated as:
\ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
\ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
where:
\itemize{
@@ -6627,9 +6607,7 @@ Later, the work of Bartlett A \emph{et al.} about bacterial pathogens infecting
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, is calculated as:
\ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
\ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
where:
\itemize{
man/antibiogram.Rd
+15 -33
View File
@@ -232,48 +232,26 @@ You can also use functions from specific 'table reporting' packages to transform
}
\section{Why Use WISCA?}{
WISCA, as outlined by Barbieri \emph{et al.} (\doi{10.1186/s13756-021-00939-2}), stands for
Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability
of adequate empirical antimicrobial regimen coverage for specific infection syndromes.
This method leverages a Bayesian hierarchical logistic regression framework with random
effects for pathogens and regimens, enabling robust estimates in the presence of sparse
data.
The Bayesian model assumes conjugate priors for parameter estimation. For example, the
coverage probability \ifelse{latex}{\deqn{$theta$}}{$theta$} for a given antimicrobial regimen
is modeled using a Beta distribution as a prior:
WISCA, as outlined by Barbieri \emph{et al.} (\doi{10.1186/s13756-021-00939-2}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian hierarchical logistic regression framework with random effects for pathogens and regimens, enabling robust estimates in the presence of sparse data.
\ifelse{latex}{\deqn{$theta$ \sim \text{Beta}($alpha$_0, $beta$_0)}}{
\ifelse{html}{\figure{beta_prior.png}{options: width="300" alt="Beta prior"}}{$theta$ ~ Beta($alpha$_0, $beta$_0)}}
The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
where \eqn{$alpha$_0} and \eqn{$beta$_0} represent prior successes and failures, respectively,
informed by expert knowledge or weakly informative priors (e.g., \eqn{$alpha$_0 = 1, $beta$_0 = 1}).
\deqn{\theta \sim \text{Beta}(\alpha_0, \beta_0)}
The likelihood function is constructed based on observed data, where the number of covered
cases for a regimen follows a binomial distribution:
where \eqn{\alpha_0} and \eqn{\beta_0} represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \eqn{\alpha_0 = 1, \beta_0 = 1}). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:
\ifelse{latex}{\deqn{y \sim \text{Binomial}(n, $theta$)}}{
\ifelse{html}{\figure{binomial_likelihood.png}{options: width="300" alt="Binomial likelihood"}}{y ~ Binomial(n, $theta$)}}
\deqn{y \sim \text{Binomial}(n, \theta)}
Posterior parameter estimates are obtained by combining the prior and likelihood using
Bayes' theorem. The posterior distribution of \eqn{$theta$} is also a Beta distribution:
Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \eqn{\theta} is also a Beta distribution:
\ifelse{latex}{\deqn{$theta$ | y \sim \text{Beta}($alpha$_0 + y, $beta$_0 + n - y)}}{
\ifelse{html}{\figure{posterior_beta.png}{options: width="300" alt="Beta posterior"}}{$theta$ | y ~ Beta($alpha$_0 + y, $beta$_0 + n - y)}}
\deqn{\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)}
For hierarchical modeling, pathogen-level effects (e.g., differences in resistance
patterns) and regimen-level effects are modelled using Gaussian priors on log-odds.
This hierarchical structure ensures partial pooling of estimates across groups,
improving stability in strata with small sample sizes. The model is implemented using
Hamiltonian Monte Carlo (HMC) sampling.
For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
Stratified results are provided based on covariates such as age, sex, and clinical
complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities).
For example, posterior odds ratios (ORs) are derived to quantify the effect of these
covariates on coverage probabilities:
Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using \code{dplyr}'s \code{\link[=group_by]{group_by()}} as a pre-processing step before running \code{\link[=wisca]{wisca()}}. In this case, posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
\ifelse{latex}{\deqn{\text{OR}_{\text{covariate}} = \frac{\exp($beta$_{\text{covariate}})}{\exp($beta$_0)}}}{
\ifelse{html}{\figure{odds_ratio.png}{options: width="300" alt="Odds ratio formula"}}{OR_covariate = exp(beta_covariate) / exp(beta_0)}}
\deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}}
By combining empirical data with prior knowledge, WISCA overcomes the limitations
of traditional combination antibiograms, offering disease-specific, patient-stratified
@@ -360,7 +338,8 @@ antibiogram(example_isolates,
ureido <- antibiogram(example_isolates,
antibiotics = ureidopenicillins(),
ab_transform = "name"
ab_transform = "name",
wisca = TRUE
)
# in an Rmd file, you would just need to return `ureido` in a chunk,
@@ -394,3 +373,6 @@ plot(ab1)
plot(ab2)
}
}
\author{
Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
}
man/as.mo.Rd
+1 -3
View File
@@ -169,9 +169,7 @@ This is based on:
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, is calculated as:
\ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
\ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
where:
\itemize{
man/mo_matching_score.Rd
+1 -3
View File
@@ -23,9 +23,7 @@ Later, the work of Bartlett A \emph{et al.} about bacterial pathogens infecting
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, is calculated as:
\ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{
\ifelse{html}{\figure{mo_matching_score.png}{options: width="300" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}
where:
\itemize{
tests/testthat.R
-3
View File
@@ -37,9 +37,6 @@ expect_inherits <- function(x, y, ...) {
", required is class ", paste0(y, collapse = "/")))
}
expect_stdout <- expect_output
if (getRversion() < "4.0.0") {
deparse1 <- AMR:::deparse1
}
# start unit tests
test_check("AMR")