Built site for AMR@3.0.1.9061: 0c1709c

2026-06-24 13:36:21 +02:00 · 2026-06-23 18:00:14 +00:00
parent 9447e0f2c2
commit 2a7bfb9ffb
328 changed files with 5357 additions and 3687 deletions
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -1,7 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram"><meta name="description" content="Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
-Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports."><meta property="og:description" content="Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
-Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports."><meta property="og:image" content="https://amr-for-r.org/logo.svg"><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></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Generate Antibiograms (WISCA, Traditional, Combination, or Syndromic) — antibiogram • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Generate Antibiograms (WISCA, Traditional, Combination, or Syndromic) — antibiogram"><meta name="description" content='Generate antibiograms from antimicrobial susceptibility data, with support for traditional, combination, syndromic, and WISCA (Weighted-Incidence Syndromic Combination Antibiogram) methods.
+For empirical therapy guidance, WISCA is the recommended approach. When initiating empirical treatment, the causative pathogen is unknown, and the clinically relevant question is: "what is the probability that this regimen will cover whatever pathogen turns out to cause the infection?" WISCA answers that question directly by weighting susceptibility by pathogen incidence within a syndrome and providing credible intervals via Bayesian Monte Carlo simulation. Traditional antibiograms remain appropriate for tracking resistance per species for surveillance purposes. See the section Explaining WISCA on this page and the WISCA vignette for details.
+All antibiogram types adhere to previously described approaches (see Source), and the WISCA method implements the Bayesian decision model by Bielicki et al. (2016, doi:10.1093/jac/dkv397
+). Output formats include plots and tables, ideal for integration with R Markdown and Quarto reports.'><meta property="og:description" content='Generate antibiograms from antimicrobial susceptibility data, with support for traditional, combination, syndromic, and WISCA (Weighted-Incidence Syndromic Combination Antibiogram) methods.
+For empirical therapy guidance, WISCA is the recommended approach. When initiating empirical treatment, the causative pathogen is unknown, and the clinically relevant question is: "what is the probability that this regimen will cover whatever pathogen turns out to cause the infection?" WISCA answers that question directly by weighting susceptibility by pathogen incidence within a syndrome and providing credible intervals via Bayesian Monte Carlo simulation. Traditional antibiograms remain appropriate for tracking resistance per species for surveillance purposes. See the section Explaining WISCA on this page and the WISCA vignette for details.
+All antibiogram types adhere to previously described approaches (see Source), and the WISCA method implements the Bayesian decision model by Bielicki et al. (2016, doi:10.1093/jac/dkv397
+). Output formats include plots and tables, ideal for integration with R Markdown and Quarto reports.'><meta property="og:image" content="https://amr-for-r.org/logo.svg"><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></head><body>
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@@ -9,7 +13,7 @@ Adhering to previously described approaches (see Source) and especially the Baye

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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9057</small>
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@@ -46,35 +50,65 @@ Adhering to previously described approaches (see Source) and especially the Baye
 </nav><div class="container template-reference-topic">
 <div class="row">
  <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.svg" class="logo" alt=""><h1>Generate Traditional, Combination, Syndromic, or WISCA Antibiograms</h1>
+      <img src="../logo.svg" class="logo" alt=""><h1>Generate Antibiograms (WISCA, Traditional, Combination, or Syndromic)</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/R/antibiogram.R" class="external-link"><code>R/antibiogram.R</code></a></small>
      <div class="d-none name"><code>antibiogram.Rd</code></div>
    </div>

    <div class="ref-description section level2">
-    <p>Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.</p>
-<p>Adhering to previously described approaches (see <em>Source</em>) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki <em>et al.</em>, these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.</p>
+    <p>Generate antibiograms from antimicrobial susceptibility data, with support for traditional, combination, syndromic, and WISCA (Weighted-Incidence Syndromic Combination Antibiogram) methods.</p>
+<p><strong>For empirical therapy guidance, WISCA is the recommended approach.</strong> When initiating empirical treatment, the causative pathogen is unknown, and the clinically relevant question is: <em>"what is the probability that this regimen will cover whatever pathogen turns out to cause the infection?"</em> WISCA answers that question directly by weighting susceptibility by pathogen incidence within a syndrome and providing credible intervals via Bayesian Monte Carlo simulation. Traditional antibiograms remain appropriate for tracking resistance per species for surveillance purposes. See the section <em>Explaining WISCA</em> on this page and the <a href="https://amr-for-r.org/articles/WISCA.html">WISCA vignette</a> for details.</p>
+<p>All antibiogram types adhere to previously described approaches (see <em>Source</em>), and the WISCA method implements the Bayesian decision model by Bielicki <em>et al.</em> (2016, <a href="https://doi.org/10.1093/jac/dkv397" class="external-link">doi:10.1093/jac/dkv397</a>
+). Output formats include plots and tables, ideal for integration with R Markdown and Quarto reports.</p>
    </div>

    <div class="section level2">
    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">x</span>, antimicrobials <span class="op">=</span> <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>, mo_transform <span class="op">=</span> <span class="st">"shortname"</span>,</span>
-<span>  ab_transform <span class="op">=</span> <span class="st">"name"</span>, syndromic_group <span class="op">=</span> <span class="cn">NULL</span>, add_total_n <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  only_all_tested <span class="op">=</span> <span class="cn">FALSE</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">wisca</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>,</span>
-<span>  formatting_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_antibiogram_formatting_type"</span>,</span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">wisca</span>, <span class="fl">14</span>, <span class="fl">18</span><span class="op">)</span><span class="op">)</span>, col_mo <span class="op">=</span> <span class="cn">NULL</span>, language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>  minimum <span class="op">=</span> <span class="fl">30</span>, combine_SI <span class="op">=</span> <span class="cn">TRUE</span>, sep <span class="op">=</span> <span class="st">" + "</span>, sort_columns <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  wisca <span class="op">=</span> <span class="cn">FALSE</span>, simulations <span class="op">=</span> <span class="fl">1000</span>, conf_interval <span class="op">=</span> <span class="fl">0.95</span>,</span>
-<span>  interval_side <span class="op">=</span> <span class="st">"two-tailed"</span>, info <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/interactive.html" class="external-link">interactive</a></span><span class="op">(</span><span class="op">)</span>, parallel <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  <span class="va">...</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">wisca</span><span class="op">(</span><span class="va">x</span>, antimicrobials <span class="op">=</span> <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>, ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span>
-<span>  syndromic_group <span class="op">=</span> <span class="cn">NULL</span>, only_all_tested <span class="op">=</span> <span class="cn">FALSE</span>, digits <span class="op">=</span> <span class="fl">1</span>,</span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">wisca</span><span class="op">(</span></span>
+<span>  <span class="va">x</span>,</span>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
+<span>  ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span>
+<span>  syndromic_group <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  only_all_tested <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  digits <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>  formatting_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_antibiogram_formatting_type"</span>, <span class="fl">14</span><span class="op">)</span>,</span>
-<span>  col_mo <span class="op">=</span> <span class="cn">NULL</span>, language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>, combine_SI <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  sep <span class="op">=</span> <span class="st">" + "</span>, sort_columns <span class="op">=</span> <span class="cn">TRUE</span>, simulations <span class="op">=</span> <span class="fl">1000</span>,</span>
-<span>  conf_interval <span class="op">=</span> <span class="fl">0.95</span>, interval_side <span class="op">=</span> <span class="st">"two-tailed"</span>,</span>
-<span>  info <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/interactive.html" class="external-link">interactive</a></span><span class="op">(</span><span class="op">)</span>, parallel <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span>
+<span>  col_mo <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  combine_SI <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  sep <span class="op">=</span> <span class="st">" + "</span>,</span>
+<span>  sort_columns <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  simulations <span class="op">=</span> <span class="fl">1000</span>,</span>
+<span>  conf_interval <span class="op">=</span> <span class="fl">0.95</span>,</span>
+<span>  interval_side <span class="op">=</span> <span class="st">"two-tailed"</span>,</span>
+<span>  info <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/interactive.html" class="external-link">interactive</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  parallel <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu">antibiogram</span><span class="op">(</span></span>
+<span>  <span class="va">x</span>,</span>
+<span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
+<span>  mo_transform <span class="op">=</span> <span class="st">"shortname"</span>,</span>
+<span>  ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span>
+<span>  syndromic_group <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  add_total_n <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  only_all_tested <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">wisca</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>,</span>
+<span>  formatting_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_antibiogram_formatting_type"</span>, <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">wisca</span>, <span class="fl">14</span>, <span class="fl">18</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>  col_mo <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  minimum <span class="op">=</span> <span class="fl">30</span>,</span>
+<span>  combine_SI <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  sep <span class="op">=</span> <span class="st">" + "</span>,</span>
+<span>  sort_columns <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  wisca <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  simulations <span class="op">=</span> <span class="fl">1000</span>,</span>
+<span>  conf_interval <span class="op">=</span> <span class="fl">0.95</span>,</span>
+<span>  interval_side <span class="op">=</span> <span class="st">"two-tailed"</span>,</span>
+<span>  info <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/interactive.html" class="external-link">interactive</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  parallel <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">retrieve_wisca_parameters</span><span class="op">(</span><span class="va">wisca_model</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
@@ -82,22 +116,31 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span><span class="fu"><a href="plot.html">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># S3 method for class 'antibiogram'</span></span>
-<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  geom <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"pointrange"</span>, <span class="st">"point"</span>, <span class="st">"col"</span>, <span class="st">"bar"</span>, <span class="st">"errorbar"</span><span class="op">)</span>,</span>
+<span>  ci <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  sort <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  flip <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  caption <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu">wisca_plot</span><span class="op">(</span></span>
+<span>  <span class="va">wisca_model</span>,</span>
+<span>  wisca_plot_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"susceptibility_incidence"</span>, <span class="st">"posterior_coverage"</span><span class="op">)</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># S3 method for class 'antibiogram'</span></span>
-<span><span class="fu">knit_print</span><span class="op">(</span><span class="va">x</span>, italicise <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  na <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"knitr.kable.NA"</span>, default <span class="op">=</span> <span class="st">""</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+<span><span class="fu">knit_print</span><span class="op">(</span></span>
+<span>  <span class="va">x</span>,</span>
+<span>  italicise <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  na <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"knitr.kable.NA"</span>, default <span class="op">=</span> <span class="st">""</span><span class="op">)</span>,</span>
+<span>  <span class="va">...</span></span>
+<span><span class="op">)</span></span></code></pre></div>
    </div>

-    <div class="section level2">
-    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
-
-<ul><li><p>Bielicki JA <em>et al.</em> (2016). <strong>Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data</strong> <em>Journal of Antimicrobial Chemotherapy</em> 71(3); <a href="https://doi.org/10.1093/jac/dkv397" class="external-link">doi:10.1093/jac/dkv397</a></p></li>
-<li><p>Bielicki JA <em>et al.</em> (2020). <strong>Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries</strong> <em>JAMA Netw Open.</em> 3(2):e1921124; <a href="https://doi.org/10.1001/jamanetworkopen.2019.21124" class="external-link">doi:10.1001/jamanetworkopen.2019.21124</a></p></li>
-<li><p>Klinker KP <em>et al.</em> (2021). <strong>Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms</strong>. <em>Therapeutic Advances in Infectious Disease</em>, May 5;8:20499361211011373; <a href="https://doi.org/10.1177/20499361211011373" class="external-link">doi:10.1177/20499361211011373</a></p></li>
-<li><p>Barbieri E <em>et al.</em> (2021). <strong>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</strong> <em>Antimicrobial Resistance &amp; Infection Control</em> May 1;10(1):74; <a href="https://doi.org/10.1186/s13756-021-00939-2" class="external-link">doi:10.1186/s13756-021-00939-2</a></p></li>
-<li><p><strong>M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition</strong>, 2022, <em>Clinical and Laboratory Standards Institute (CLSI)</em>. <a href="https://clsi.org/standards/products/microbiology/documents/m39/" class="external-link">https://clsi.org/standards/products/microbiology/documents/m39/</a>.</p></li>
-</ul></div>
    <div class="section level2">
    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>

@@ -123,20 +166,12 @@ Adhering to previously described approaches (see Source) and especially the Baye
 </ul></dd>


-<dt id="arg-mo-transform">mo_transform<a class="anchor" aria-label="anchor" href="#arg-mo-transform"></a></dt>
-<dd><p>A character to transform microorganism input - must be <code>"name"</code>, <code>"shortname"</code> (default), <code>"gramstain"</code>, or one of the column names of the <a href="microorganisms.html">microorganisms</a> data set: <code>"mo"</code>, <code>"fullname"</code>, <code>"status"</code>, <code>"kingdom"</code>, <code>"phylum"</code>, <code>"class"</code>, <code>"order"</code>, <code>"family"</code>, <code>"genus"</code>, <code>"species"</code>, <code>"subspecies"</code>, <code>"rank"</code>, <code>"ref"</code>, <code>"oxygen_tolerance"</code>, <code>"source"</code>, <code>"lpsn"</code>, <code>"lpsn_parent"</code>, <code>"lpsn_renamed_to"</code>, <code>"mycobank"</code>, <code>"mycobank_parent"</code>, <code>"mycobank_renamed_to"</code>, <code>"gbif"</code>, <code>"gbif_parent"</code>, <code>"gbif_renamed_to"</code>, <code>"prevalence"</code>, or <code>"snomed"</code>. Can also be <code>NULL</code> to not transform the input or <code>NA</code> to consider all microorganisms 'unknown'.</p></dd>
-
-
 <dt id="arg-ab-transform">ab_transform<a class="anchor" aria-label="anchor" href="#arg-ab-transform"></a></dt>
 <dd><p>A character to transform antimicrobial input - must be one of the column names of the <a href="antimicrobials.html">antimicrobials</a> data set (defaults to <code>"name"</code>): <code>"ab"</code>, <code>"cid"</code>, <code>"name"</code>, <code>"group"</code>, <code>"atc"</code>, <code>"atc_group1"</code>, <code>"atc_group2"</code>, <code>"abbreviations"</code>, <code>"synonyms"</code>, <code>"oral_ddd"</code>, <code>"oral_units"</code>, <code>"iv_ddd"</code>, <code>"iv_units"</code>, or <code>"loinc"</code>. Can also be <code>NULL</code> to not transform the input.</p></dd>


 <dt id="arg-syndromic-group">syndromic_group<a class="anchor" aria-label="anchor" href="#arg-syndromic-group"></a></dt>
-<dd><p>A column name of `x`, or values calculated to split rows of `x`, e.g. by using [ifelse()] or [`case_when()`][dplyr::case_when()]. See *Examples*.</p></dd>
-
-
-<dt id="arg-add-total-n">add_total_n<a class="anchor" aria-label="anchor" href="#arg-add-total-n"></a></dt>
-<dd><p><em>(deprecated in favour of <code>formatting_type</code>)</em> A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether <code>n_tested</code> available numbers per pathogen should be added to the table (default is <code>TRUE</code>). This will add the lowest and highest number of available isolates per antimicrobial (e.g, if for <em>E. coli</em> 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). This option is unavailable when <code>wisca = TRUE</code>; in that case, use <code>retrieve_wisca_parameters()</code> to get the parameters used for WISCA.</p></dd>
+<dd><p>A column name of <code>x</code>, or values calculated to split rows of <code>x</code>, e.g. by using <code><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse()</a></code> or <code><a href="https://dplyr.tidyverse.org/reference/case-and-replace-when.html" class="external-link">case_when()</a></code>. See <em>Examples</em>.</p></dd>


 <dt id="arg-only-all-tested">only_all_tested<a class="anchor" aria-label="anchor" href="#arg-only-all-tested"></a></dt>
@@ -159,10 +194,6 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <dd><p>Language to translate text, which defaults to the system language (see <code><a href="translate.html">get_AMR_locale()</a></code>).</p></dd>


-<dt id="arg-minimum">minimum<a class="anchor" aria-label="anchor" href="#arg-minimum"></a></dt>
-<dd><p>The minimum allowed number of available (tested) isolates. Any isolate count lower than <code>minimum</code> will return <code>NA</code> with a warning. The default number of <code>30</code> isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see <em>Source</em>.</p></dd>
-
-
 <dt id="arg-combine-si">combine_SI<a class="anchor" aria-label="anchor" href="#arg-combine-si"></a></dt>
 <dd><p>A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether all susceptibility should be determined by results of either S, SDD, or I, instead of only S (default is <code>TRUE</code>).</p></dd>

@@ -175,10 +206,6 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <dd><p>A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether the antimicrobial columns must be sorted on name.</p></dd>


-<dt id="arg-wisca">wisca<a class="anchor" aria-label="anchor" href="#arg-wisca"></a></dt>
-<dd><p>A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is <code>FALSE</code>). This will use a Bayesian decision model to estimate regimen coverage probabilities using <a href="https://en.wikipedia.org/wiki/Monte_Carlo_method" class="external-link">Monte Carlo simulations</a>. Set <code>simulations</code>, <code>conf_interval</code>, and <code>interval_side</code> to adjust.</p></dd>
-
-
 <dt id="arg-simulations">simulations<a class="anchor" aria-label="anchor" href="#arg-simulations"></a></dt>
 <dd><p>(for WISCA) a numerical value to set the number of Monte Carlo simulations.</p></dd>

@@ -200,7 +227,25 @@ Adhering to previously described approaches (see Source) and especially the Baye


 <dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
-<dd><p>When used in <a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">R Markdown or Quarto</a>: arguments passed on to <code><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">knitr::kable()</a></code> (otherwise, has no use).</p></dd>
+<dd><p>Currently unused.</p></dd>
+
+
+<dt id="arg-mo-transform">mo_transform<a class="anchor" aria-label="anchor" href="#arg-mo-transform"></a></dt>
+<dd><p>A character to transform microorganism input - must be <code>"name"</code>, <code>"shortname"</code> (default), <code>"gramstain"</code>, or one of the column names of the <a href="microorganisms.html">microorganisms</a> data set: <code>"mo"</code>, <code>"fullname"</code>, <code>"status"</code>, <code>"domain"</code>, <code>"kingdom"</code>, <code>"phylum"</code>, <code>"class"</code>, <code>"order"</code>, <code>"family"</code>, <code>"genus"</code>, <code>"species"</code>, <code>"subspecies"</code>, <code>"rank"</code>, <code>"ref"</code>, <code>"oxygen_tolerance"</code>, <code>"morphology"</code>, <code>"source"</code>, <code>"lpsn"</code>, <code>"lpsn_parent"</code>, <code>"lpsn_renamed_to"</code>, <code>"mycobank"</code>, <code>"mycobank_parent"</code>, <code>"mycobank_renamed_to"</code>, <code>"gbif"</code>, <code>"gbif_parent"</code>, <code>"gbif_renamed_to"</code>, <code>"prevalence"</code>, or <code>"snomed"</code>. Can also be <code>NULL</code> to not transform the input or <code>NA</code> to consider all microorganisms 'unknown'.</p></dd>
+
+
+<dt id="arg-add-total-n">add_total_n<a class="anchor" aria-label="anchor" href="#arg-add-total-n"></a></dt>
+<dd><p><em>(deprecated in favour of <code>formatting_type</code>)</em> A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether <code>n_tested</code> available numbers per pathogen should be added to the table (default is <code>TRUE</code>). This will add the lowest and highest number of available isolates per antimicrobial (e.g., if for <em>E. coli</em> 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). This option is unavailable when <code>wisca = TRUE</code>; in that case, use <code>retrieve_wisca_parameters()</code> to get the parameters used for WISCA.</p></dd>
+
+
+<dt id="arg-minimum">minimum<a class="anchor" aria-label="anchor" href="#arg-minimum"></a></dt>
+<dd><p>The minimum allowed number of available (tested) isolates. Any isolate count lower than <code>minimum</code> will return <code>NA</code> with a warning. The default number of <code>30</code> isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see <em>Source</em>.</p></dd>
+
+
+<dt id="arg-wisca">wisca<a class="anchor" aria-label="anchor" href="#arg-wisca"></a></dt>
+<dd><p>A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is <code>FALSE</code>). This will use a Bayesian decision model to estimate regimen coverage probabilities using <a href="https://en.wikipedia.org/wiki/Monte_Carlo_method" class="external-link">Monte Carlo simulations</a>. Per <a href="https://doi.org/10.1093/jac/dkv397" class="external-link">doi:10.1093/jac/dkv397</a>
+, susceptibility priors are \(\beta(0.5, 0.5)\) (Jeffreys) and intrinsically resistant pairs (based on <a href="intrinsic_resistant.html">intrinsic_resistant</a>) use \(\beta(1, 9999)\).</p>
+<p>Set <code>simulations</code>, <code>conf_interval</code>, and <code>interval_side</code> to adjust.</p></dd>


 <dt id="arg-wisca-model">wisca_model<a class="anchor" aria-label="anchor" href="#arg-wisca-model"></a></dt>
@@ -211,6 +256,30 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <dd><p>An <code>antibiogram()</code> object.</p></dd>


+<dt id="arg-geom">geom<a class="anchor" aria-label="anchor" href="#arg-geom"></a></dt>
+<dd><p>The plotting style for the point estimate. One of <code>"pointrange"</code> (default), <code>"point"</code>, <code>"col"</code>/<code>"bar"</code>, or <code>"errorbar"</code>. <code>"pointrange"</code> is recommended for coverage data: bars imply a meaningful baseline at zero, which coverage estimates rarely have.</p></dd>
+
+
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
+<dd><p>Logical, whether to draw the credible/confidence interval. Defaults to <code>TRUE</code>. Ignored (forced <code>TRUE</code>) when <code>geom = "pointrange"</code> or <code>"errorbar"</code>, since the interval is intrinsic to those geoms.</p></dd>
+
+
+<dt id="arg-sort">sort<a class="anchor" aria-label="anchor" href="#arg-sort"></a></dt>
+<dd><p>Logical, whether to order regimens by coverage. Defaults to <code>TRUE</code>. When faceted (per pathogen) or grouped (syndromic), ordering is applied within each panel/group.</p></dd>
+
+
+<dt id="arg-flip">flip<a class="anchor" aria-label="anchor" href="#arg-flip"></a></dt>
+<dd><p>Logical, whether to draw regimens on the y-axis (horizontal). Defaults to <code>NULL</code>, which flips automatically when any regimen label exceeds 20 characters (long combination names read poorly on the x-axis). Set <code>TRUE</code>/<code>FALSE</code> to override.</p></dd>
+
+
+<dt id="arg-caption">caption<a class="anchor" aria-label="anchor" href="#arg-caption"></a></dt>
+<dd><p>Text to show as caption, will explain non-inferiority for WISCA models.</p></dd>
+
+
+<dt id="arg-wisca-plot-type">wisca_plot_type<a class="anchor" aria-label="anchor" href="#arg-wisca-plot-type"></a></dt>
+<dd><p>Either <code>"susceptibility_incidence"</code> (default) or <code>"posterior_coverage"</code>.</p></dd>
+
+
 <dt id="arg-italicise">italicise<a class="anchor" aria-label="anchor" href="#arg-italicise"></a></dt>
 <dd><p>A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether the microorganism names in the <a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">knitr</a> table should be made italic, using <code><a href="italicise_taxonomy.html">italicise_taxonomy()</a></code>.</p></dd>

@@ -255,40 +324,15 @@ Adhering to previously described approaches (see Source) and especially the Baye
 </div>

 <div class="section">
-<h3 id="antibiogram-types">Antibiogram Types<a class="anchor" aria-label="anchor" href="#antibiogram-types"></a></h3>
+<h3 id="when-to-use-wisca-vs-traditional-antibiograms">When to Use WISCA vs. Traditional Antibiograms<a class="anchor" aria-label="anchor" href="#when-to-use-wisca-vs-traditional-antibiograms"></a></h3>


 <p>There are various antibiogram types, as summarised by Klinker <em>et al.</em> (2021, <a href="https://doi.org/10.1177/20499361211011373" class="external-link">doi:10.1177/20499361211011373</a>
-), and they are all supported by <code>antibiogram()</code>.</p>
-<p>For clinical coverage estimations, <strong>use WISCA whenever possible</strong>, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki <em>et al.</em> (2020, <a href="https://doi.org/10.1001/jamanetworkopen.2019.21124" class="external-link">doi:10.1001/jamanetworkopen.2019.21124</a>
-). See the section <em>Explaining WISCA</em> on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.</p><ol><li><p><strong>Traditional Antibiogram</strong></p>
-<p>Case example: Susceptibility of <em>Pseudomonas aeruginosa</em> to piperacillin/tazobactam (TZP)</p>
-<p>Code example:</p>
-<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
-<span>            antimicrobials <span class="op">=</span> <span class="st">"TZP"</span><span class="op">)</span></span></code></pre><p></p></div></li>
-<li><p><strong>Combination Antibiogram</strong></p>
-<p>Case example: Additional susceptibility of <em>Pseudomonas aeruginosa</em> to TZP + tobramycin versus TZP alone</p>
-<p>Code example:</p>
-<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
-<span>            antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre><p></p></div></li>
-<li><p><strong>Syndromic Antibiogram</strong></p>
-<p>Case example: Susceptibility of <em>Pseudomonas aeruginosa</em> to TZP among respiratory specimens (obtained among ICU patients only)</p>
-<p>Code example:</p>
-<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
-<span>            antimicrobials <span class="op">=</span> <span class="fu"><a href="../reference/antimicrobial_selectors.html">penicillins</a></span><span class="op">(</span><span class="op">)</span>,</span>
-<span>            syndromic_group <span class="op">=</span> <span class="st">"ward"</span><span class="op">)</span></span></code></pre><p></p></div></li>
-<li><p><strong>Weighted-Incidence Syndromic Combination Antibiogram (WISCA)</strong></p>
-<p>WISCA can be applied to any antibiogram, see the section <em>Explaining WISCA</em> on this page for more information.</p>
-<p>Code example:</p>
-<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
-<span>            antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span>
-<span>            wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># this is equal to:</span></span>
-<span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
-<span>      antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre><p></p></div>
-<p>WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre data sets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).</p></li>
-</ol></div>
+), and they are all supported by <code>antibiogram()</code>: traditional, combination, syndromic, and WISCA.</p>
+<p><strong>If your goal is to guide empirical therapy, use WISCA.</strong> Traditional antibiograms fragment susceptibility information by species, but at the point of prescribing, the clinician does not know which species is causing the infection. WISCA shifts the unit of analysis from the isolate to the patient: it estimates the probability that a regimen will cover the infection, given the local distribution of causative pathogens. It evaluates combination regimens, weights by pathogen incidence, and provides credible intervals that honestly communicate uncertainty. Hebert <em>et al.</em> (2012) demonstrated this concretely for the first time: ciprofloxacin showed 84% susceptibility against <em>E. coli</em> in the traditional antibiogram, but WISCA coverage was only 62% for UTI and 37% for abdominal infections, because other species (including intrinsically resistant enterococci) contribute substantially to these syndromes. Note that WISCA is pathogen-agnostic: the outcome is not stratified by species, but by syndrome.</p>
+<p><strong>Traditional, combination, and syndromic antibiograms remain appropriate for AMR surveillance</strong>, i.e., tracking resistance trends per species over time. They are the right tool when the question is <em>"how resistant is species X to drug Y in our setting?"</em> rather than <em>"what regimen best covers this syndrome?"</em>.</p>
+<p>All four types are demonstrated in the <em>Examples</em> section below.</p>
+</div>

 <div class="section">
 <h3 id="grouped-tibbles">Grouped tibbles<a class="anchor" aria-label="anchor" href="#grouped-tibbles"></a></h3>
@@ -302,38 +346,6 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span>  <span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span>antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre><p></p></div>
 </div>

-<div class="section">
-<h3 id="stepped-approach-for-clinical-insight">Stepped Approach for Clinical Insight<a class="anchor" aria-label="anchor" href="#stepped-approach-for-clinical-insight"></a></h3>
-
-
-<p>In clinical practice, antimicrobial coverage decisions evolve as more microbiological data becomes available. This theoretical stepped approach ensures empirical coverage can continuously assessed to improve patient outcomes:</p><ol><li><p><strong>Initial Empirical Therapy (Admission / Pre-Culture Data)</strong></p>
-<p>At admission, no pathogen information is available.</p><ul><li><p>Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms. Using the pathogen-agnostic yet incidence-weighted WISCA is preferred.</p></li>
-<li><p>Code example:</p>
-<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
-<span>            antimicrobials <span class="op">=</span> <span class="va">selected_regimens</span>,</span>
-<span>            mo_transform <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span> <span class="co"># all pathogens set to `NA`</span></span>
-<span></span>
-<span><span class="co"># preferred: use WISCA</span></span>
-<span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
-<span>      antimicrobials <span class="op">=</span> <span class="va">selected_regimens</span><span class="op">)</span></span></code></pre><p></p></div></li>
-</ul></li>
-<li><p><strong>Refinement with Gram Stain Results</strong></p>
-<p>When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).</p><ul><li><p>Action: narrow coverage based on Gram stain-specific resistance patterns.</p></li>
-<li><p>Code example:</p>
-<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
-<span>            antimicrobials <span class="op">=</span> <span class="va">selected_regimens</span>,</span>
-<span>            mo_transform <span class="op">=</span> <span class="st">"gramstain"</span><span class="op">)</span> <span class="co"># all pathogens set to Gram-pos/Gram-neg</span></span></code></pre><p></p></div></li>
-</ul></li>
-<li><p><strong>Definitive Therapy Based on Species Identification</strong></p>
-<p>After cultivation of the pathogen, full pathogen identification allows precise targeting of therapy.</p><ul><li><p>Action: adjust treatment to pathogen-specific antibiograms, minimizing resistance risks.</p></li>
-<li><p>Code example:</p>
-<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">your_data</span>,</span>
-<span>            antimicrobials <span class="op">=</span> <span class="va">selected_regimens</span>,</span>
-<span>            mo_transform <span class="op">=</span> <span class="st">"shortname"</span><span class="op">)</span> <span class="co"># all pathogens set to 'G. species', e.g., E. coli</span></span></code></pre><p></p></div></li>
-</ul></li>
-</ol><p>By structuring antibiograms around this stepped approach, clinicians can make data-driven adjustments at each stage, ensuring optimal empirical and targeted therapy while reducing unnecessary broad-spectrum antimicrobial use.</p>
-</div>
-
 <div class="section">
 <h3 id="inclusion-in-combination-antibiograms">Inclusion in Combination Antibiograms<a class="anchor" aria-label="anchor" href="#inclusion-in-combination-antibiograms"></a></h3>

@@ -372,10 +384,22 @@ Adhering to previously described approaches (see Source) and especially the Baye



-<p>WISCA (Weighted-Incidence Syndromic Combination Antibiogram) estimates the probability of empirical coverage for combination regimens.</p>
-<p>It weights susceptibility by pathogen prevalence within a clinical syndrome and provides credible intervals around the expected coverage.</p>
-<p>For more background, interpretation, and examples, see <a href="https://amr-for-r.org/articles/WISCA.html">the WISCA vignette</a>.</p>
+<p>WISCA (Weighted-Incidence Syndromic Combination Antibiogram) estimates the probability that an empirical antimicrobial regimen will provide adequate coverage for a given infection syndrome, before the causative pathogen has been identified.</p>
+<p>It does so by combining two quantities: the relative incidence of each pathogen within the syndrome (modelled as a Dirichlet distribution) and the susceptibility of each pathogen to the regimen (modelled as Beta distributions). These are combined via Monte Carlo simulation to produce a coverage estimate with a credible interval.</p>
+<p><strong>Prior distributions:</strong> Pathogen incidence uses a non-informative \(Dirichlet(1, 1, \ldots, 1)\) prior. Susceptibility proportions use the Jeffreys prior, \(\beta(0.5, 0.5)\), except for pathogen-drug combinations with known intrinsic resistance, which use a strongly informative \(\beta(1, 9999)\) prior that forces near-zero susceptibility regardless of observed data. Intrinsic resistance is determined using the <a href="intrinsic_resistant.html">intrinsic_resistant</a> data set, which is based on <a href="https://www.eucast.org/bacteria/important-additional-information/expert-rules/" class="external-link">'EUCAST Expected Resistant Phenotypes' v1.2</a> (2023).</p>
+<p><strong>Interpreting the output:</strong> Overlapping credible intervals between regimens indicate no significant difference in coverage; if a narrower-spectrum regimen overlaps with a broader one, the narrower-spectrum option may be preferred on stewardship grounds. Non-overlapping intervals indicate a clinically meaningful difference. For small sample sizes, consider pooling data from multiple sites to improve precision, provided pathogen distributions are sufficiently similar (Bielicki <em>et al.</em>, 2016).</p>
+<p>For the full mathematical derivation and worked examples, see the <a href="https://amr-for-r.org/articles/WISCA.html">WISCA vignette</a>.</p>
    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+
+<ul><li><p>Hebert C <em>et al.</em> (2012). <strong>Demonstration of the weighted-incidence syndromic combination antibiogram: an empiric prescribing decision aid.</strong> <em>Infection Control &amp; Hospital Epidemiology</em> 33(4):381-388; <a href="https://doi.org/10.1086/664768" class="external-link">doi:10.1086/664768</a></p></li>
+<li><p>Bielicki JA <em>et al.</em> (2016). <strong>Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data.</strong> <em>Journal of Antimicrobial Chemotherapy</em> 71(3):794-802; <a href="https://doi.org/10.1093/jac/dkv397" class="external-link">doi:10.1093/jac/dkv397</a></p></li>
+<li><p>Cook A <em>et al.</em> (2022). <strong>Improving empiric antibiotic prescribing in pediatric bloodstream infections: a potential application of weighted-incidence syndromic combination antibiograms (WISCA).</strong> <em>Expert Review of Anti-infective Therapy</em> 20(3):445-456; <a href="https://doi.org/10.1080/14787210.2021.1967145" class="external-link">doi:10.1080/14787210.2021.1967145</a></p></li>
+<li><p>Klinker KP <em>et al.</em> (2021). <strong>Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms.</strong> <em>Therapeutic Advances in Infectious Disease</em>, May 5;8:20499361211011373; <a href="https://doi.org/10.1177/20499361211011373" class="external-link">doi:10.1177/20499361211011373</a></p></li>
+<li><p>Barbieri E <em>et al.</em> (2021). <strong>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.</strong> <em>Antimicrobial Resistance &amp; Infection Control</em> May 1;10(1):74; <a href="https://doi.org/10.1186/s13756-021-00939-2" class="external-link">doi:10.1186/s13756-021-00939-2</a></p></li>
+<li><p><strong>M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition</strong>, 2022, <em>Clinical and Laboratory Standards Institute (CLSI)</em>. <a href="https://clsi.org/standards/products/microbiology/documents/m39/" class="external-link">https://clsi.org/standards/products/microbiology/documents/m39/</a>.</p></li>
+</ul></div>
    <div class="section level2">
    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
    <p>Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends</p>
@@ -408,7 +432,105 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, …</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \donttest{</span></span></span>
-<span class="r-in"><span><span class="co"># Traditional antibiogram ----------------------------------------------</span></span></span>
+<span class="r-in"><span><span class="co"># WISCA antibiogram (recommended for empirical therapy) -----------------</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># basic WISCA: empirical coverage per regimen, weighted by pathogen</span></span></span>
+<span class="r-in"><span><span class="co"># incidence, with 95% credible intervals</span></span></span>
+<span class="r-in"><span><span class="fu">wisca</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
+<span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC+CIP"</span>, <span class="st">"AMC+GEN"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>invalid microorganism code, NA generated</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       1 × 3</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Weighted-Incidence Syndromic Combination Antibiogram (WISCA)</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Cred. interval: 95%</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Simulations:    1000 per stratum</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   `Amoxicillin/clavulanic acid` Amoxicillin/clavulanic …¹ Amoxicillin/clavulan…²</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                         <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 74.2% (72.1-76.1%)            88.8% (87.2-90.3%)        90.8% (89.3-92.1%)    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated names: ¹`Amoxicillin/clavulanic acid + Ciprofloxacin`,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   ²`Amoxicillin/clavulanic acid + Gentamicin`</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># and use `wisca_plot()` to assess the simulation outcomes.</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Or, use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># equivalent using antibiogram():</span></span></span>
+<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
+<span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC+CIP"</span>, <span class="st">"AMC+GEN"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  wisca <span class="op">=</span> <span class="cn">TRUE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>invalid microorganism code, NA generated</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       1 × 3</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Weighted-Incidence Syndromic Combination Antibiogram (WISCA)</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Cred. interval: 95%</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Simulations:    1000 per stratum</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   `Amoxicillin/clavulanic acid` Amoxicillin/clavulanic …¹ Amoxicillin/clavulan…²</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                         <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 74.2% (72.2-76.1%)            88.8% (87.1-90.4%)        90.8% (89.4-92.2%)    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated names: ¹`Amoxicillin/clavulanic acid + Ciprofloxacin`,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   ²`Amoxicillin/clavulanic acid + Gentamicin`</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># and use `wisca_plot()` to assess the simulation outcomes.</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Or, use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># stratified by syndrome or clinical group</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">wisca</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
+<span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  syndromic_group <span class="op">=</span> <span class="st">"ward"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>invalid microorganism code, NA generated</span>
+<span class="r-in"><span><span class="va">out</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       3 × 4</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Weighted-Incidence Syndromic Combination Antibiogram (WISCA)</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Cred. interval: 95%</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Simulations:    1000 per stratum</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                             </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical          74.5% (68.8-79.8%)        93.6% (91.9-95.1%)                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU               57.1% (48.2-65.9%)        86.7% (83.3-89.9%)                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient        57.5% (46-68.7%)          76.5% (70.6-82.2%)                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated name: ¹`Piperacillin/tazobactam + Gentamicin`</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` &lt;chr&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># and use `wisca_plot()` to assess the simulation outcomes.</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Or, use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
+<span class="r-in"><span><span class="fu">wisca_plot</span><span class="op">(</span><span class="va">out</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="antibiogram-1.png" alt="" width="700" height="433"></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># stratified using grouped tibbles (e.g. by age and gender)</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"dplyr"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span>  <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span>  <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
+<span class="r-in"><span>    <span class="fu"><a href="top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span>n <span class="op">=</span> <span class="fl">10</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
+<span class="r-in"><span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>      age_group <span class="op">=</span> <span class="fu"><a href="age_groups.html">age_groups</a></span><span class="op">(</span><span class="va">age</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">25</span>, <span class="fl">50</span>, <span class="fl">75</span><span class="op">)</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>      <span class="va">gender</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
+<span class="r-in"><span>    <span class="fu">wisca</span><span class="op">(</span>antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">mo</span> as input for `col_mo`.</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Number of tested isolates should exceed 30 for each regimen (and group). WISCA</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> coverage estimates might be inaccurate.</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       8 × 5</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Weighted-Incidence Syndromic Combination Antibiogram (WISCA)</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Cred. interval: 95%</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Simulations:    1000 per stratum</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   age_group gender `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentami…¹</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                              </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 0-24      F      57.7% (29.5-82.6%)        70.5% (45.9-89.1%)                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 0-24      M      59.1% (33-84.2%)          76.1% (55.7-90.6%)                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 25-49     F      67.4% (43.3-90.5%)        93.8% (87.8-97.9%)                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> 25-49     M      56.8% (27.5-86.5%)        90.9% (82.4-96.8%)                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> 50-74     F      68% (53.3-82.3%)          96.9% (94.7-98.5%)                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">6</span> 50-74     M      67.1% (56.5-77.5%)        96.8% (94.2-98.8%)                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">7</span> 75+       F      73.3% (62.9-83.6%)        97.7% (96-98.9%)                   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">8</span> 75+       M      74% (64.2-83.1%)          97.9% (96.1-99.1%)                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated name: ¹`Piperacillin/tazobactam + Gentamicin`</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` &lt;chr&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># and use `wisca_plot()` to assess the simulation outcomes.</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Or, use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># Traditional antibiogram (for AMR surveillance) ------------------------</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
 <span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
@@ -416,8 +538,9 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 10 × 7</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Non-WISCA with 95% CI</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       10 × 7</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Traditional Antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Conf. interval: 95%</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    Pathogen       Amikacin    Gentamicin Imipenem Kanamycin Meropenem Tobramycin</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> CoNS           0% (0-8%,N… 86% (82-9… 52% (37… 0% (0-8%… 52% (37-… 22% (12-3…</span>
@@ -431,7 +554,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> S. hominis     <span style="color: #BB0000;">NA</span>          92% (84-9… <span style="color: #BB0000;">NA</span>       <span style="color: #BB0000;">NA</span>        <span style="color: #BB0000;">NA</span>        85% (74-9…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> S. pneumoniae  0% (0-3%,N… 0% (0-3%,… <span style="color: #BB0000;">NA</span>       0% (0-3%… <span style="color: #BB0000;">NA</span>        0% (0-3%,…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
 <span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
@@ -440,43 +563,26 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 5</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Non-WISCA with 95% CI</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       2 × 5</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Traditional Antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Conf. interval: 95%</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Pathogen      J01GB01            J01GB03             J01GB04         J01GB06  </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>               <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>           <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684)  0% (0-10%,N=35) 98% (96-…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
-<span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
-<span class="r-in"><span>  mo_transform <span class="op">=</span> <span class="st">"name"</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 5 × 3</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Non-WISCA with 95% CI</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   Pathogen                                 Imipenem             Meropenem       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                                    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>           </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Coagulase-negative Staphylococcus (CoNS) 52% (37-67%,N=48)    52% (37-67%,N=4…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> Enterococcus faecalis                    100% (91-100%,N=38)  <span style="color: #BB0000;">NA</span>              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Escherichia coli                         100% (99-100%,N=422) 100% (99-100%,N…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> Klebsiella pneumoniae                    100% (93-100%,N=51)  100% (93-100%,N…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> Proteus mirabilis                        94% (79-99%,N=32)    <span style="color: #BB0000;">NA</span>              </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Combined antibiogram -------------------------------------------------</span></span></span>
+<span class="r-in"><span><span class="co"># Combination antibiogram (for AMR surveillance) ------------------------</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># combined antimicrobials yield higher empiric coverage</span></span></span>
 <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
 <span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  mo_transform <span class="op">=</span> <span class="st">"gramstain"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 4</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Non-WISCA with 95% CI</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       2 × 4</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Combination Antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Conf. interval: 95%</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Pathogen  Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Gram-neg… 88% (85-91%,N=641)     99% (97-99%,N=691)     98% (97-99%,N=693)    </span>
@@ -485,7 +591,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   ²`Piperacillin/tazobactam + Gentamicin`,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   ³`Piperacillin/tazobactam + Tobramycin`</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># you can use any antimicrobial selector with `+` too:</span></span></span>
 <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
@@ -493,8 +599,9 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-in"><span>  mo_transform <span class="op">=</span> <span class="st">"gramstain"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `ureidopenicillins()` using column <span style="color: #00BB00; font-weight: bold;">TZP</span> (piperacillin/tazobactam)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 4</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Non-WISCA with 95% CI</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       2 × 4</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Combination Antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Conf. interval: 95%</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Pathogen  Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Gram-neg… 88% (85-91%,N=641)     99% (97-99%,N=691)     98% (97-99%,N=693)    </span>
@@ -503,7 +610,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   ²`Piperacillin/tazobactam + Gentamicin`,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   ³`Piperacillin/tazobactam + Tobramycin`</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># names of antimicrobials do not need to resemble columns exactly:</span></span></span>
 <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
@@ -512,19 +619,19 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-in"><span>  ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
 <span class="r-in"><span>  sep <span class="op">=</span> <span class="st">" &amp; "</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 3</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Non-WISCA with 95% CI</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       2 × 3</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Traditional Antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Conf. interval: 95%</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Pathogen      Ciprofloxacin      `Ciprofloxacin &amp; Gentamicin`</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694)          </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847)          </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># Syndromic antibiogram ------------------------------------------------</span></span></span>
+<span class="r-in"><span><span class="co"># Syndromic antibiogram (for AMR surveillance) --------------------------</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># the data set could contain a filter for e.g. respiratory specimens</span></span></span>
 <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
 <span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  syndromic_group <span class="op">=</span> <span class="st">"ward"</span></span></span>
@@ -532,8 +639,9 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 14 × 8</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Non-WISCA with 95% CI</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       14 × 8</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Syndromic Antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Conf. interval: 95%</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    `Syndromic Group` Pathogen   Amikacin Gentamicin Imipenem Kanamycin Meropenem</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> Clinical          CoNS       <span style="color: #BB0000;">NA</span>       89% (84-9… 57% (39… <span style="color: #BB0000;">NA</span>        57% (39-…</span>
@@ -552,14 +660,12 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">14</span> ICU               S. pneumo… 0% (0-1… 0% (0-12%… <span style="color: #BB0000;">NA</span>       0% (0-12… <span style="color: #BB0000;">NA</span>       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: Tobramycin &lt;chr&gt;</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># now define a data set with only E. coli</span></span></span>
-<span class="r-in"><span><span class="va">ex1</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="mo_property.html">mo_genus</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Escherichia"</span><span class="op">)</span>, <span class="op">]</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">mo</span> as input for `mo_genus()`</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># with a custom language, though this will be determined automatically</span></span></span>
 <span class="r-in"><span><span class="co"># (i.e., this table will be in Spanish on Spanish systems)</span></span></span>
+<span class="r-in"><span><span class="va">ex1</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="mo_property.html">mo_genus</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Escherichia"</span><span class="op">)</span>, <span class="op">]</span></span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">mo</span> as input for `mo_genus()`</span>
 <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">ex1</span>,</span></span>
 <span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
@@ -570,45 +676,25 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 5</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Non-WISCA with 95% CI</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble:       2 × 5</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           Syndromic Antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Conf. interval: 95%</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   `Grupo sindrómico` Patógeno Amikacina            Gentamicina       Tobramicina</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> No UCI             E. coli  100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> UCI                E. coli  100% (93-100%,N=52)  99% (95-100%,N=1… 96% (92-99…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># WISCA antibiogram ----------------------------------------------------</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co"># WISCA are not stratified by species, but rather on syndromes</span></span></span>
-<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
-<span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  syndromic_group <span class="op">=</span> <span class="st">"ward"</span>,</span></span>
-<span class="r-in"><span>  wisca <span class="op">=</span> <span class="cn">TRUE</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 3 × 4</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type:           WISCA with 95% CI</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                             </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical          73.4% (68.3-78.6%)        92.3% (90.7-93.7%)                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU               57.4% (49.7-65.4%)        84.9% (82.1-87.6%)                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient        57% (47.4-66.7%)          74.6% (68.8-79.8%)                </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated name: ¹`Piperacillin/tazobactam + Gentamicin`</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` &lt;chr&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see `antibiogram()`.</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Print the output for R Markdown / Quarto -----------------------------</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">ureido</span> <span class="op">&lt;-</span> <span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
+<span class="r-in"><span><span class="va">ureido</span> <span class="op">&lt;-</span> <span class="fu">wisca</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
 <span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="antimicrobial_selectors.html">ureidopenicillins</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  syndromic_group <span class="op">=</span> <span class="st">"ward"</span>,</span></span>
-<span class="r-in"><span>  wisca <span class="op">=</span> <span class="cn">TRUE</span></span></span>
+<span class="r-in"><span>  syndromic_group <span class="op">=</span> <span class="st">"ward"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `ureidopenicillins()` using column <span style="color: #00BB00; font-weight: bold;">TZP</span> (piperacillin/tazobactam)</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>invalid microorganism code, NA generated</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># in an Rmd file, you would just need to return `ureido` in a chunk,</span></span></span>
 <span class="r-in"><span><span class="co"># but to be explicit here:</span></span></span>
@@ -619,9 +705,9 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> |Syndromic Group |Piperacillin/tazobactam |</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> |:---------------|:-----------------------|</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |Clinical        |73.5% (68-79%)          |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |ICU             |57.7% (49.9-65.3%)      |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> |Outpatient      |56.9% (46.5-66.8%)      |</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> |Clinical        |74.6% (68.9-80%)        |</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> |ICU             |57% (49.1-65.8%)        |</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> |Outpatient      |57.4% (45.6-68.4%)      |</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># Generate plots with ggplot2 or base R --------------------------------</span></span></span>
@@ -630,25 +716,26 @@ Adhering to previously described approaches (see Source) and especially the Baye
 <span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  mo_transform <span class="op">=</span> <span class="st">"gramstain"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">ab2</span> <span class="op">&lt;-</span> <span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
+<span class="r-in"><span><span class="va">ab2</span> <span class="op">&lt;-</span> <span class="fu">wisca</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
 <span class="r-in"><span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span></span>
 <span class="r-in"><span>  syndromic_group <span class="op">=</span> <span class="st">"ward"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>invalid microorganism code, NA generated</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"ggplot2"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">ab1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-plt img"><img src="antibiogram-1.png" alt="" width="700" height="433"></span>
+<span class="r-plt img"><img src="antibiogram-2.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"ggplot2"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">ab2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-plt img"><img src="antibiogram-2.png" alt="" width="700" height="433"></span>
+<span class="r-plt img"><img src="antibiogram-3.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu"><a href="plot.html">plot</a></span><span class="op">(</span><span class="va">ab1</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="antibiogram-3.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="fu"><a href="plot.html">plot</a></span><span class="op">(</span><span class="va">ab2</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="antibiogram-4.png" alt="" width="700" height="433"></span>
+<span class="r-in"><span><span class="fu"><a href="plot.html">plot</a></span><span class="op">(</span><span class="va">ab2</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="antibiogram-5.png" alt="" width="700" height="433"></span>
+<span class="r-plt img"><img src="antibiogram-6.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
    </div>