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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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< 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" >
< 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 > )
< 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 > )
< 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 >
< / pre > < / div >
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< / 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 >
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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 >
< 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 >
< span id = "cb2-3" > < a href = "#cb2-3" > < / a > < / span >
< span id = "cb2-4" > < a href = "#cb2-4" > < / a > < span class = "co" > # or:< / span > < / span >
< span id = "cb2-5" > < a href = "#cb2-5" > < / a > example_isolates < span class = "op" > %> %< / span > < span class = "st" > < / span > < / span >
< 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 >
< span id = "cb2-7" > < a href = "#cb2-7" > < / a > model < span class = "st" > "binomial"< / span > )< / span >
< span id = "cb2-8" > < a href = "#cb2-8" > < / a > < / span >
< span id = "cb2-9" > < a href = "#cb2-9" > < / a > < span class = "co" > # to bind it to object 'predict_TZP' for example:< / span > < / span >
< 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 >
< 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 >
< 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 >
< 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" >
< 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 >
< span class = "co" > # 2 2003 0.06250000 NA NA 32 0.06250000 0.06163839< / span >
< span class = "co" > # 3 2004 0.08536585 NA NA 82 0.08536585 0.06760841< / span >
< span class = "co" > # 4 2005 0.05000000 NA NA 60 0.05000000 0.07411100< / span >
< span class = "co" > # 5 2006 0.05084746 NA NA 59 0.05084746 0.08118454< / span >
< span class = "co" > # 6 2007 0.12121212 NA NA 66 0.12121212 0.08886843< / span >
< span class = "co" > # 7 2008 0.04166667 NA NA 72 0.04166667 0.09720264< / span >
< span class = "co" > # 8 2009 0.01639344 NA NA 61 0.01639344 0.10622731< / span >
< span class = "co" > # 9 2010 0.05660377 NA NA 53 0.05660377 0.11598223< / span >
< span class = "co" > # 10 2011 0.18279570 NA NA 93 0.18279570 0.12650615< / span >
< span class = "co" > # 11 2012 0.30769231 NA NA 65 0.30769231 0.13783610< / span >
< span class = "co" > # 12 2013 0.06896552 NA NA 58 0.06896552 0.15000651< / span >
< span class = "co" > # 13 2014 0.10000000 NA NA 60 0.10000000 0.16304829< / span >
< span class = "co" > # 14 2015 0.23636364 NA NA 55 0.23636364 0.17698785< / span >
< span class = "co" > # 15 2016 0.22619048 NA NA 84 0.22619048 0.19184597< / span >
< span class = "co" > # 16 2017 0.16279070 NA NA 86 0.16279070 0.20763675< / span >
< span class = "co" > # 17 2018 0.22436641 0.1938710 0.2548618 NA NA 0.22436641< / span >
< span class = "co" > # 18 2019 0.24203228 0.2062911 0.2777735 NA NA 0.24203228< / span >
< span class = "co" > # 19 2020 0.26062172 0.2191758 0.3020676 NA NA 0.26062172< / span >
< span class = "co" > # 20 2021 0.28011130 0.2325557 0.3276669 NA NA 0.28011130< / span >
< span class = "co" > # 21 2022 0.30046606 0.2464567 0.3544755 NA NA 0.30046606< / span >
< span class = "co" > # 22 2023 0.32163907 0.2609011 0.3823771 NA NA 0.32163907< / span >
< span class = "co" > # 23 2024 0.34357130 0.2759081 0.4112345 NA NA 0.34357130< / span >
< span class = "co" > # 24 2025 0.36619175 0.2914934 0.4408901 NA NA 0.36619175< / span >
< span class = "co" > # 25 2026 0.38941799 0.3076686 0.4711674 NA NA 0.38941799< / span >
< span class = "co" > # 26 2027 0.41315710 0.3244399 0.5018743 NA NA 0.41315710< / span >
< span class = "co" > # 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688< / span >
< 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 >
< / 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" >
< span class = "fu" > < a href = "https://rdrr.io/r/graphics/plot.default.html" > plot< / a > < / span > (< span class = "kw" > predict_TZP< / span > )
< / pre > < / div >
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< 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 >
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< div class = "sourceCode" id = "cb6" > < pre class = "downlit" >
< span class = "fu" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > (< span class = "kw" > predict_TZP< / span > )
< / 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 > )
< / pre > < / div >
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< 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 >
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< p > Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. < / p >
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< div class = "sourceCode" id = "cb8" > < 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" > "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 >
< / 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 >
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< 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 >
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< 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 >
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< td > < code > < a href = "https://rdrr.io/r/stats/glm.html" > glm(..., family = binomial)< / a > < / code > < / td >
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< td > Generalised linear model with binomial distribution< / td >
< / tr >
< tr class = "even" >
< td >
< code > "loglin"< / code > or < code > "poisson"< / code >
< / td >
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< td > < code > < a href = "https://rdrr.io/r/stats/glm.html" > glm(..., family = poisson)< / a > < / code > < / td >
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< td > Generalised linear model with poisson distribution< / td >
< / tr >
< tr class = "odd" >
< td >
< code > "lin"< / code > or < code > "linear"< / code >
< / td >
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< td > < code > < a href = "https://rdrr.io/r/stats/lm.html" > lm()< / a > < / code > < / td >
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< td > Linear model< / td >
< / tr >
< / tbody >
< / table >
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< 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 >
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< 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 >
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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 >
< / pre > < / div >
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< p > < img src = "resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width = "720" > < / p >
< p > This seems more likely, doesn’ < / p >
< p > The model itself is also available from the object, as an < code > attribute< / code > :< / p >
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< 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 >
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< 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 >
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< span class = "co" > # < / span >
< span class = "co" > # Family: binomial < / span >
< span class = "co" > # Link function: logit< / span >
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< 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 >
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< 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 >
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< span class = "co" > # year 0.09883005 0.02295317 4.305725 1.664395e-05< / span >
< / pre > < / div >
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< nav id = "toc" data-toggle = "toc" > < h2 data-toc-skip > Contents< / h2 >
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< p > Site built with < a href = "https://pkgdown.r-lib.org/" > pkgdown< / a > 1.5.1.9000.< / p >
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