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aria-label="Search for..." autocomplete="off"> </div> </form> </div> <!--/.nav-collapse --> </div> <!--/.container --> </div> <!--/.navbar --> </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1>How to predict antimicrobial resistance</h1> <h4 class="author">Matthijs S. Berends</h4> <h4 class="date">16 October 2019</h4> <div class="hidden name"><code>resistance_predict.Rmd</code></div> </div> <div id="needed-r-packages" class="section level2"> <h2 class="hasAnchor"> <a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2> <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> <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="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(dplyr)</a> <a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(ggplot2)</a> <a class="sourceLine" id="cb1-3" data-line-number="3"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(AMR)</a> <a class="sourceLine" id="cb1-4" data-line-number="4"></a> <a class="sourceLine" id="cb1-5" data-line-number="5"><span class="co"># (if not yet installed, install with:)</span></a> <a class="sourceLine" id="cb1-6" data-line-number="6"><span class="co"># install.packages(c("tidyverse", "AMR"))</span></a></code></pre></div> </div> <div id="prediction-analysis" class="section level2"> <h2 class="hasAnchor"> <a href="#prediction-analysis" class="anchor"></a>Prediction analysis</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 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="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></a> <a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></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>)</a> <a class="sourceLine" id="cb2-3" data-line-number="3"></a> <a class="sourceLine" id="cb2-4" data-line-number="4"><span class="co"># or:</span></a> <a class="sourceLine" id="cb2-5" data-line-number="5">example_isolates <span class="op">%>%</span><span class="st"> </span></a> <a class="sourceLine" id="cb2-6" data-line-number="6"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</a> <a class="sourceLine" id="cb2-7" data-line-number="7"> model <span class="st">"binomial"</span>)</a> <a class="sourceLine" id="cb2-8" data-line-number="8"></a> <a class="sourceLine" id="cb2-9" data-line-number="9"><span class="co"># to bind it to object 'predict_TZP' for example:</span></a> <a class="sourceLine" id="cb2-10" data-line-number="10">predict_TZP <-<span class="st"> </span>example_isolates <span class="op">%>%</span><span class="st"> </span></a> <a class="sourceLine" id="cb2-11" data-line-number="11"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</a> <a class="sourceLine" id="cb2-12" data-line-number="12"> <span class="dt">model =</span> <span class="st">"binomial"</span>)</a></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><a href="../reference/resistance_predict.html">resistance_predict(..., info = FALSE)</a></code>.</p> <pre><code># NOTE: Using column `date` as input for `col_date`. # # Logistic regression model (logit) with binomial distribution # ------------------------------------------------------------ # # Call: # glm(formula = df_matrix ~ year, family = binomial) # # Deviance Residuals: # Min 1Q Median 3Q Max # -2.6817 -1.4087 -0.5657 0.9672 3.5728 # # Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -224.39872 48.03354 -4.672 2.99e-06 *** # year 0.11061 0.02388 4.633 3.61e-06 *** # --- # Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # # (Dispersion parameter for binomial family taken to be 1) # # Null deviance: 61.512 on 14 degrees of freedom # Residual deviance: 38.692 on 13 degrees of freedom # AIC: 95.212 # # Number of Fisher Scoring iterations: 4</code></pre> <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="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">predict_TZP</a> <a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co"># year value se_min se_max observations observed estimated</span></a> <a class="sourceLine" id="cb4-3" data-line-number="3"><span class="co"># 1 2003 0.06250000 NA NA 32 0.06250000 0.05486389</span></a> <a class="sourceLine" id="cb4-4" data-line-number="4"><span class="co"># 2 2004 0.08536585 NA NA 82 0.08536585 0.06089002</span></a> <a class="sourceLine" id="cb4-5" data-line-number="5"><span class="co"># 3 2005 0.05000000 NA NA 60 0.05000000 0.06753075</span></a> <a class="sourceLine" id="cb4-6" data-line-number="6"><span class="co"># 4 2006 0.05084746 NA NA 59 0.05084746 0.07483801</span></a> <a class="sourceLine" id="cb4-7" data-line-number="7"><span class="co"># 5 2007 0.12121212 NA NA 66 0.12121212 0.08286570</span></a> <a class="sourceLine" id="cb4-8" data-line-number="8"><span class="co"># 6 2008 0.04166667 NA NA 72 0.04166667 0.09166918</span></a> <a class="sourceLine" id="cb4-9" data-line-number="9"><span class="co"># 7 2009 0.01639344 NA NA 61 0.01639344 0.10130461</span></a> <a class="sourceLine" id="cb4-10" data-line-number="10"><span class="co"># 8 2010 0.05660377 NA NA 53 0.05660377 0.11182814</span></a> <a class="sourceLine" id="cb4-11" data-line-number="11"><span class="co"># 9 2011 0.18279570 NA NA 93 0.18279570 0.12329488</span></a> <a class="sourceLine" id="cb4-12" data-line-number="12"><span class="co"># 10 2012 0.30769231 NA NA 65 0.30769231 0.13575768</span></a> <a class="sourceLine" id="cb4-13" data-line-number="13"><span class="co"># 11 2013 0.06896552 NA NA 58 0.06896552 0.14926576</span></a> <a class="sourceLine" id="cb4-14" data-line-number="14"><span class="co"># 12 2014 0.10000000 NA NA 60 0.10000000 0.16386307</span></a> <a class="sourceLine" id="cb4-15" data-line-number="15"><span class="co"># 13 2015 0.23636364 NA NA 55 0.23636364 0.17958657</span></a> <a class="sourceLine" id="cb4-16" data-line-number="16"><span class="co"># 14 2016 0.22619048 NA NA 84 0.22619048 0.19646431</span></a> <a class="sourceLine" id="cb4-17" data-line-number="17"><span class="co"># 15 2017 0.16279070 NA NA 86 0.16279070 0.21451350</span></a> <a class="sourceLine" id="cb4-18" data-line-number="18"><span class="co"># 16 2018 0.23373852 0.2021578 0.2653193 NA NA 0.23373852</span></a> <a class="sourceLine" id="cb4-19" data-line-number="19"><span class="co"># 17 2019 0.25412909 0.2168525 0.2914057 NA NA 0.25412909</span></a> <a class="sourceLine" id="cb4-20" data-line-number="20"><span class="co"># 18 2020 0.27565854 0.2321869 0.3191302 NA NA 0.27565854</span></a> <a class="sourceLine" id="cb4-21" data-line-number="21"><span class="co"># 19 2021 0.29828252 0.2481942 0.3483709 NA NA 0.29828252</span></a> <a class="sourceLine" id="cb4-22" data-line-number="22"><span class="co"># 20 2022 0.32193804 0.2649008 0.3789753 NA NA 0.32193804</span></a> <a class="sourceLine" id="cb4-23" data-line-number="23"><span class="co"># 21 2023 0.34654311 0.2823269 0.4107593 NA NA 0.34654311</span></a> <a class="sourceLine" id="cb4-24" data-line-number="24"><span class="co"># 22 2024 0.37199700 0.3004860 0.4435080 NA NA 0.37199700</span></a> <a class="sourceLine" id="cb4-25" data-line-number="25"><span class="co"># 23 2025 0.39818127 0.3193839 0.4769787 NA NA 0.39818127</span></a> <a class="sourceLine" id="cb4-26" data-line-number="26"><span class="co"># 24 2026 0.42496142 0.3390173 0.5109056 NA NA 0.42496142</span></a> <a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># 25 2027 0.45218939 0.3593720 0.5450068 NA NA 0.45218939</span></a> <a class="sourceLine" id="cb4-28" data-line-number="28"><span class="co"># 26 2028 0.47970658 0.3804212 0.5789920 NA NA 0.47970658</span></a> <a class="sourceLine" id="cb4-29" data-line-number="29"><span class="co"># 27 2029 0.50734745 0.4021241 0.6125708 NA NA 0.50734745</span></a></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="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw"><a href="https://rdrr.io/r/graphics/plot.html">plot</a></span>(predict_TZP)</a></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_rsi_predict()</a></code> to create more appealing plots:</p> <div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_TZP)</a></code></pre></div> <p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p> <div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"></a> <a class="sourceLine" id="cb7-2" data-line-number="2"><span class="co"># choose for error bars instead of a ribbon</span></a> <a class="sourceLine" id="cb7-3" data-line-number="3"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_TZP, <span class="dt">ribbon =</span> <span class="ot">FALSE</span>)</a></code></pre></div> <p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p> <div id="choosing-the-right-model" class="section level3"> <h3 class="hasAnchor"> <a href="#choosing-the-right-model" class="anchor"></a>Choosing the right model</h3> <p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:</p> <div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">example_isolates <span class="op">%>%</span></a> <a class="sourceLine" id="cb8-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo, <span class="dt">language =</span> <span class="ot">NULL</span>) <span class="op">==</span><span class="st"> "Gram-positive"</span>) <span class="op">%>%</span></a> <a class="sourceLine" id="cb8-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"VAN"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>) <span class="op">%>%</span><span class="st"> </span></a> <a class="sourceLine" id="cb8-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()</a> <a class="sourceLine" id="cb8-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></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 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 (left half of a) binomial distribution is to be expected based on the observed years:</p> <div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1">example_isolates <span class="op">%>%</span></a> <a class="sourceLine" id="cb9-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo, <span class="dt">language =</span> <span class="ot">NULL</span>) <span class="op">==</span><span class="st"> "Gram-positive"</span>) <span class="op">%>%</span></a> <a class="sourceLine" id="cb9-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"VAN"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>, <span class="dt">model =</span> <span class="st">"linear"</span>) <span class="op">%>%</span><span class="st"> </span></a> <a class="sourceLine" id="cb9-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()</a> <a class="sourceLine" id="cb9-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></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="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1">model <-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/attributes.html">attributes</a></span>(predict_TZP)<span class="op">$</span>model</a> <a class="sourceLine" id="cb10-2" data-line-number="2"></a> <a class="sourceLine" id="cb10-3" data-line-number="3"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(model)<span class="op">$</span>family</a> <a class="sourceLine" id="cb10-4" data-line-number="4"><span class="co"># </span></a> <a class="sourceLine" id="cb10-5" data-line-number="5"><span class="co"># Family: binomial </span></a> <a class="sourceLine" id="cb10-6" data-line-number="6"><span class="co"># Link function: logit</span></a> <a class="sourceLine" id="cb10-7" data-line-number="7"></a> <a class="sourceLine" id="cb10-8" data-line-number="8"><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(model)<span class="op">$</span>coefficients</a> <a class="sourceLine" id="cb10-9" data-line-number="9"><span class="co"># Estimate Std. Error z value Pr(>|z|)</span></a> <a class="sourceLine" id="cb10-10" data-line-number="10"><span class="co"># (Intercept) -224.3987194 48.0335384 -4.671709 2.987038e-06</span></a> <a class="sourceLine" id="cb10-11" data-line-number="11"><span class="co"># year 0.1106102 0.0238753 4.632831 3.606990e-06</span></a></code></pre></div> </div> </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <div id="tocnav"> <h2 class="hasAnchor"> <a href="#tocnav" class="anchor"></a>Contents</h2> <ul class="nav nav-pills nav-stacked"> <li><a href="#needed-r-packages">Needed R packages</a></li> <li><a href="#prediction-analysis">Prediction analysis</a></li> </ul> </div> </div> </div> <footer><div class="copyright"> <p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alex W. 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