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<img src="../logo.svg" class="logo" alt=""><h1>How to predict antimicrobial resistance</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/resistance_predict.Rmd" class="external-link"><code>vignettes/resistance_predict.Rmd</code></a></small>
<div class="d-none name"><code>resistance_predict.Rmd</code></div>
</div>
<div class="section level2">
<h2 id="needed-r-packages">Needed R packages<a class="anchor" aria-label="anchor" href="#needed-r-packages"></a>
</h2>
<p>As with many uses in R, we need some additional packages for AMR data
analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></a>. The
tidyverse tremendously improves the way we conduct data science - it
allows for a very natural way of writing syntaxes and creating beautiful
plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even
extends their use and functions.</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><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="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://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
<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://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span></span>
<span></span>
<span><span class="co"># (if not yet installed, install with:)</span></span>
<span><span class="co"># install.packages(c("tidyverse", "AMR"))</span></span></code></pre></div>
</div>
<div class="section level2">
<h2 id="prediction-analysis">Prediction analysis<a class="anchor" aria-label="anchor" href="#prediction-analysis"></a>
</h2>
<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>,
which takes the same input as functions for <a href="./AMR.html">other
AMR data analysis</a>. Based on a date column, it calculates cases per
year and uses a regression model to predict antimicrobial
resistance.</p>
<p>It is basically as easy as:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
<span><span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>tbl <span class="op">=</span> <span class="va">example_isolates</span>, col_date <span class="op">=</span> <span class="st">"date"</span>, col_ab <span class="op">=</span> <span class="st">"TZP"</span>, model <span class="op">=</span> <span class="st">"binomial"</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># or:</span></span>
<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="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span></span>
<span> col_ab <span class="op">=</span> <span class="st">"TZP"</span>,</span>
<span> model <span class="op">=</span> <span class="st">"binomial"</span></span>
<span> <span class="op">)</span></span>
<span></span>
<span><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
<span><span class="va">predict_TZP</span> <span class="op">&lt;-</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="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span></span>
<span> col_ab <span class="op">=</span> <span class="st">"TZP"</span>,</span>
<span> model <span class="op">=</span> <span class="st">"binomial"</span></span>
<span> <span class="op">)</span></span></code></pre></div>
<p>The function will look for a date column itself if
<code>col_date</code> is not set.</p>
<p>When running any of these commands, a summary of the regression model
will be printed unless using
<code>resistance_predict(..., info = FALSE)</code>.</p>
<p>This text is only a printed summary - the actual result (output) of
the function is a <code>data.frame</code> containing for each year: the
number of observations, the actual observed resistance, the estimated
resistance and the standard error below and above the estimation:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">predict_TZP</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 33 × 7</span></span></span>
<span><span class="co">#&gt; year value se_min se_max observations observed estimated</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> <span style="text-decoration: underline;">2</span>002 0.2 <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 15 0.2 0.056<span style="text-decoration: underline;">2</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> <span style="text-decoration: underline;">2</span>003 0.062<span style="text-decoration: underline;">5</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 32 0.062<span style="text-decoration: underline;">5</span> 0.061<span style="text-decoration: underline;">6</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> <span style="text-decoration: underline;">2</span>004 0.085<span style="text-decoration: underline;">4</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 82 0.085<span style="text-decoration: underline;">4</span> 0.067<span style="text-decoration: underline;">6</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> <span style="text-decoration: underline;">2</span>005 0.05 <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 60 0.05 0.074<span style="text-decoration: underline;">1</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> <span style="text-decoration: underline;">2</span>006 0.050<span style="text-decoration: underline;">8</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 59 0.050<span style="text-decoration: underline;">8</span> 0.081<span style="text-decoration: underline;">2</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> <span style="text-decoration: underline;">2</span>007 0.121 <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 66 0.121 0.088<span style="text-decoration: underline;">9</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> <span style="text-decoration: underline;">2</span>008 0.041<span style="text-decoration: underline;">7</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 72 0.041<span style="text-decoration: underline;">7</span> 0.097<span style="text-decoration: underline;">2</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> <span style="text-decoration: underline;">2</span>009 0.016<span style="text-decoration: underline;">4</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 61 0.016<span style="text-decoration: underline;">4</span> 0.106 </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> <span style="text-decoration: underline;">2</span>010 0.056<span style="text-decoration: underline;">6</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 53 0.056<span style="text-decoration: underline;">6</span> 0.116 </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> <span style="text-decoration: underline;">2</span>011 0.183 <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 93 0.183 0.127 </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 23 more rows</span></span></span></code></pre></div>
<p>The function <code>plot</code> is available in base R, and can be
extended by other packages to depend the output based on the type of
input. We extended its function to cope with resistance predictions:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
<p>This is the fastest way to plot the result. It automatically adds the
right axes, error bars, titles, number of available observations and
type of model.</p>
<p>We also support the <code>ggplot2</code> package with our custom
function <code><a href="../reference/resistance_predict.html">ggplot_sir_predict()</a></code> to create more appealing
plots:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/resistance_predict.html">ggplot_sir_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span></span>
<span><span class="co"># choose for error bars instead of a ribbon</span></span>
<span><span class="fu"><a href="../reference/resistance_predict.html">ggplot_sir_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p>
<div class="section level3">
<h3 id="choosing-the-right-model">Choosing the right model<a class="anchor" aria-label="anchor" href="#choosing-the-right-model"></a>
</h3>
<p>Resistance is not easily predicted; if we look at vancomycin
resistance in Gram-positive bacteria, the spread (i.e. standard error)
is enormous:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><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="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</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="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"binomial"</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="fu"><a href="../reference/resistance_predict.html">ggplot_sir_predict</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
<p>Vancomycin resistance could be 100% in ten years, but might remain
very low.</p>
<p>You can define the model with the <code>model</code> parameter. The
model chosen above is a generalised linear regression model using a
binomial distribution, assuming that a period of zero resistance was
followed by a period of increasing resistance leading slowly to more and
more resistance.</p>
<p>Valid values are:</p>
<table class="table">
<colgroup>
<col width="32%">
<col width="25%">
<col width="42%">
</colgroup>
<thead><tr class="header">
<th>Input values</th>
<th>Function used by R</th>
<th>Type of model</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>
<code>"binomial"</code> or <code>"binom"</code> or
<code>"logit"</code>
</td>
<td><code>glm(..., family = binomial)</code></td>
<td>Generalised linear model with binomial distribution</td>
</tr>
<tr class="even">
<td>
<code>"loglin"</code> or <code>"poisson"</code>
</td>
<td><code>glm(..., family = poisson)</code></td>
<td>Generalised linear model with poisson distribution</td>
</tr>
<tr class="odd">
<td>
<code>"lin"</code> or <code>"linear"</code>
</td>
<td><code><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm()</a></code></td>
<td>Linear model</td>
</tr>
</tbody>
</table>
<p>For the vancomycin resistance in Gram-positive bacteria, a linear
model might be more appropriate:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><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="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</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="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"linear"</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="fu"><a href="../reference/resistance_predict.html">ggplot_sir_predict</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
<p>The model itself is also available from the object, as an
<code>attribute</code>:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span></span>
<span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">family</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Family: binomial </span></span>
<span><span class="co">#&gt; Link function: logit</span></span></code></pre></div>
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">coefficients</span></span>
<span><span class="co">#&gt; Estimate Std. Error z value Pr(&gt;|z|)</span></span>
<span><span class="co">#&gt; (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05</span></span>
<span><span class="co">#&gt; year 0.09883005 0.02295317 4.305725 1.664395e-05</span></span></code></pre></div>
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