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</header><script src="resistance_predict_files/header-attrs-2.3/header-attrs.js"></script><script src="resistance_predict_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
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<h1 data-toc-skip>How to predict antimicrobial resistance</h1>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/resistance_predict.Rmd"><code>vignettes/resistance_predict.Rmd</code></a></small>
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<div class="hidden name"><code>resistance_predict.Rmd</code></div>
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<div id="needed-r-packages" class="section level2">
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<h2 class="hasAnchor">
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<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
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<p>As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the <a href="https://www.tidyverse.org">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org"><code>ggplot2</code></a> by Dr Hadley Wickham. 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>
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<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
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<div class="sourceCode" id="cb1"><pre class="downlit">
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<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://dplyr.tidyverse.org">dplyr</a></span>)
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<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="http://ggplot2.tidyverse.org">ggplot2</a></span>)
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<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://msberends.github.io/AMR">AMR</a></span>)
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<span class="co"># (if not yet installed, install with:)</span>
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<span class="co"># install.packages(c("tidyverse", "AMR"))</span>
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</pre></div>
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</div>
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<div id="prediction-analysis" class="section level2">
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<h2 class="hasAnchor">
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<a href="#prediction-analysis" class="anchor"></a>Prediction analysis</h2>
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<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 analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
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<p>It is basically as easy as:</p>
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<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
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<span id="cb2-2"><a href="#cb2-2"></a><span class="kw">resistance_predict</span>(<span class="dt">tbl =</span> example_isolates, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>)</span>
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<span id="cb2-3"><a href="#cb2-3"></a></span>
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<span id="cb2-4"><a href="#cb2-4"></a><span class="co"># or:</span></span>
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<span id="cb2-5"><a href="#cb2-5"></a>example_isolates <span class="op">%>%</span><span class="st"> </span></span>
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<span id="cb2-6"><a href="#cb2-6"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
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<span id="cb2-7"><a href="#cb2-7"></a> model <span class="st">"binomial"</span>)</span>
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<span id="cb2-8"><a href="#cb2-8"></a></span>
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<span id="cb2-9"><a href="#cb2-9"></a><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
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<span id="cb2-10"><a href="#cb2-10"></a>predict_TZP <-<span class="st"> </span>example_isolates <span class="op">%>%</span><span class="st"> </span></span>
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<span id="cb2-11"><a href="#cb2-11"></a><span class="st"> </span><span class="kw">resistance_predict</span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</span>
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<span id="cb2-12"><a href="#cb2-12"></a> <span class="dt">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
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<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
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<p>When running any of these commands, a summary of the regression model will be printed unless using <code><a href="../reference/resistance_predict.html">resistance_predict(..., info = FALSE)</a></code>.</p>
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<pre><code># NOTE: Using column `date` as input for `col_date`.</code></pre>
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<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>
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<div class="sourceCode" id="cb4"><pre class="downlit">
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<span class="kw">predict_TZP</span>
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<span class="co"># year value se_min se_max observations observed estimated</span>
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<span class="co"># 1 2002 0.20000000 NA NA 15 0.20000000 0.05616378</span>
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<span class="co"># 2 2003 0.06250000 NA NA 32 0.06250000 0.06163839</span>
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<span class="co"># 3 2004 0.08536585 NA NA 82 0.08536585 0.06760841</span>
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<span class="co"># 4 2005 0.05000000 NA NA 60 0.05000000 0.07411100</span>
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<span class="co"># 5 2006 0.05084746 NA NA 59 0.05084746 0.08118454</span>
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<span class="co"># 6 2007 0.12121212 NA NA 66 0.12121212 0.08886843</span>
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<span class="co"># 7 2008 0.04166667 NA NA 72 0.04166667 0.09720264</span>
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<span class="co"># 8 2009 0.01639344 NA NA 61 0.01639344 0.10622731</span>
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<span class="co"># 9 2010 0.05660377 NA NA 53 0.05660377 0.11598223</span>
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<span class="co"># 10 2011 0.18279570 NA NA 93 0.18279570 0.12650615</span>
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<span class="co"># 11 2012 0.30769231 NA NA 65 0.30769231 0.13783610</span>
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<span class="co"># 12 2013 0.06896552 NA NA 58 0.06896552 0.15000651</span>
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<span class="co"># 13 2014 0.10000000 NA NA 60 0.10000000 0.16304829</span>
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<span class="co"># 14 2015 0.23636364 NA NA 55 0.23636364 0.17698785</span>
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<span class="co"># 15 2016 0.22619048 NA NA 84 0.22619048 0.19184597</span>
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<span class="co"># 16 2017 0.16279070 NA NA 86 0.16279070 0.20763675</span>
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<span class="co"># 17 2018 0.22436641 0.1938710 0.2548618 NA NA 0.22436641</span>
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<span class="co"># 18 2019 0.24203228 0.2062911 0.2777735 NA NA 0.24203228</span>
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<span class="co"># 19 2020 0.26062172 0.2191758 0.3020676 NA NA 0.26062172</span>
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<span class="co"># 20 2021 0.28011130 0.2325557 0.3276669 NA NA 0.28011130</span>
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<span class="co"># 21 2022 0.30046606 0.2464567 0.3544755 NA NA 0.30046606</span>
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<span class="co"># 22 2023 0.32163907 0.2609011 0.3823771 NA NA 0.32163907</span>
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<span class="co"># 23 2024 0.34357130 0.2759081 0.4112345 NA NA 0.34357130</span>
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<span class="co"># 24 2025 0.36619175 0.2914934 0.4408901 NA NA 0.36619175</span>
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<span class="co"># 25 2026 0.38941799 0.3076686 0.4711674 NA NA 0.38941799</span>
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<span class="co"># 26 2027 0.41315710 0.3244399 0.5018743 NA NA 0.41315710</span>
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<span class="co"># 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688</span>
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<span class="co"># 28 2029 0.46175755 0.3597639 0.5637512 NA NA 0.46175755</span>
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<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span>
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</pre></div>
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<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>
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<div class="sourceCode" id="cb5"><pre class="downlit">
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<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="kw">predict_TZP</span>)
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</pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
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<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>
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<p>We also support the <code>ggplot2</code> package with our custom function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing plots:</p>
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<div class="sourceCode" id="cb6"><pre class="downlit">
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<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(<span class="kw">predict_TZP</span>)
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</pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
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<div class="sourceCode" id="cb7"><pre class="downlit">
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<span class="co"># choose for error bars instead of a ribbon</span>
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<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(<span class="kw">predict_TZP</span>, ribbon = <span class="fl">FALSE</span>)
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</pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p>
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<div id="choosing-the-right-model" class="section level3">
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<h3 class="hasAnchor">
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<a href="#choosing-the-right-model" class="anchor"></a>Choosing the right model</h3>
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<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>
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<div class="sourceCode" id="cb8"><pre class="downlit">
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<span class="kw">example_isolates</span> <span class="op">%>%</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span>(<span class="kw">mo</span>, language = <span class="kw">NULL</span>) <span class="op">==</span> <span class="st">"Gram-positive"</span>) <span class="op">%>%</span>
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<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(col_ab = <span class="st">"VAN"</span>, year_min = <span class="fl">2010</span>, info = <span class="fl">FALSE</span>, model = <span class="st">"binomial"</span>) <span class="op">%>%</span>
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<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()
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<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
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</pre></div>
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<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 also stay around 0%.</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><a href="https://rdrr.io/r/stats/glm.html">glm(..., family = binomial)</a></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><a href="https://rdrr.io/r/stats/glm.html">glm(..., family = poisson)</a></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">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 since no binomial distribution is to be expected based on the observed years:</p>
|
||
<div class="sourceCode" id="cb9"><pre class="downlit">
|
||
<span class="kw">example_isolates</span> <span class="op">%>%</span>
|
||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span>(<span class="kw">mo</span>, language = <span class="kw">NULL</span>) <span class="op">==</span> <span class="st">"Gram-positive"</span>) <span class="op">%>%</span>
|
||
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(col_ab = <span class="st">"VAN"</span>, year_min = <span class="fl">2010</span>, info = <span class="fl">FALSE</span>, model = <span class="st">"linear"</span>) <span class="op">%>%</span>
|
||
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()
|
||
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
|
||
</pre></div>
|
||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
|
||
<p>This seems more likely, doesn’t it?</p>
|
||
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
|
||
<div class="sourceCode" id="cb10"><pre class="downlit">
|
||
<span class="kw">model</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span>(<span class="kw">predict_TZP</span>)<span class="op">$</span><span class="kw">model</span>
|
||
|
||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="kw">model</span>)<span class="op">$</span><span class="kw">family</span>
|
||
<span class="co"># </span>
|
||
<span class="co"># Family: binomial </span>
|
||
<span class="co"># Link function: logit</span>
|
||
|
||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="kw">model</span>)<span class="op">$</span><span class="kw">coefficients</span>
|
||
<span class="co"># Estimate Std. Error z value Pr(>|z|)</span>
|
||
<span class="co"># (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05</span>
|
||
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span>
|
||
</pre></div>
|
||
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|
||
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|
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