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< h1 > How to predict antimicrobial resistance< / h1 >
< h4 class = "author" > Matthijs S. Berends< / h4 >
2019-06-01 21:28:18 +02:00
< h4 class = "date" > 01 June 2019< / h4 >
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< 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 < a href = "https://www.linkedin.com/in/hadleywickham/" > Dr Hadley Wickham< / 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 = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb1-1" title = "1" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/library" > library< / a > < / span > (dplyr)< / a >
< a class = "sourceLine" id = "cb1-2" title = "2" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/library" > library< / a > < / span > (ggplot2)< / a >
< a class = "sourceLine" id = "cb1-3" title = "3" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/library" > library< / a > < / span > (AMR)< / a >
< a class = "sourceLine" id = "cb1-4" title = "4" > < / a >
< a class = "sourceLine" id = "cb1-5" title = "5" > < span class = "co" > # (if not yet installed, install with:)< / span > < / a >
< a class = "sourceLine" id = "cb1-6" title = "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 >
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< div class = "sourceCode" id = "cb2" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb2-1" title = "1" > < span class = "co" > # resistance prediction of piperacillin/tazobactam (TZP):< / span > < / a >
< a class = "sourceLine" id = "cb2-2" title = "2" > < span class = "kw" > < a href = "../reference/resistance_predict.html" > resistance_predict< / a > < / span > (< span class = "dt" > tbl =< / span > septic_patients, < span class = "dt" > col_date =< / span > < span class = "st" > "date"< / span > , < span class = "dt" > col_ab =< / span > < span class = "st" > "TZP"< / span > )< / a >
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< a class = "sourceLine" id = "cb2-3" title = "3" > < / a >
< a class = "sourceLine" id = "cb2-4" title = "4" > < span class = "co" > # or:< / span > < / a >
< a class = "sourceLine" id = "cb2-5" title = "5" > septic_patients < span class = "op" > %> %< / span > < span class = "st" > < / span > < / a >
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< a class = "sourceLine" id = "cb2-6" title = "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 >
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< a class = "sourceLine" id = "cb2-7" title = "7" > < / a >
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< a class = "sourceLine" id = "cb2-8" title = "8" > < span class = "co" > # to bind it to object 'predict_TZP' for example:< / span > < / a >
< a class = "sourceLine" id = "cb2-9" title = "9" > predict_TZP < -< span class = "st" > < / span > septic_patients < span class = "op" > %> %< / span > < span class = "st" > < / span > < / a >
< a class = "sourceLine" id = "cb2-10" title = "10" > < 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 > < / 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`.
#
# 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 >
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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 = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb4-1" title = "1" > predict_TZP< / a >
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< a class = "sourceLine" id = "cb4-2" title = "2" > < span class = "co" > # year value se_min se_max observations observed estimated< / span > < / a >
< a class = "sourceLine" id = "cb4-3" title = "3" > < span class = "co" > # 1 2003 0.06250000 NA NA 32 0.06250000 0.05486389< / span > < / a >
< a class = "sourceLine" id = "cb4-4" title = "4" > < span class = "co" > # 2 2004 0.08536585 NA NA 82 0.08536585 0.06089002< / span > < / a >
< a class = "sourceLine" id = "cb4-5" title = "5" > < span class = "co" > # 3 2005 0.05000000 NA NA 60 0.05000000 0.06753075< / span > < / a >
< a class = "sourceLine" id = "cb4-6" title = "6" > < span class = "co" > # 4 2006 0.05084746 NA NA 59 0.05084746 0.07483801< / span > < / a >
< a class = "sourceLine" id = "cb4-7" title = "7" > < span class = "co" > # 5 2007 0.12121212 NA NA 66 0.12121212 0.08286570< / span > < / a >
< a class = "sourceLine" id = "cb4-8" title = "8" > < span class = "co" > # 6 2008 0.04166667 NA NA 72 0.04166667 0.09166918< / span > < / a >
< a class = "sourceLine" id = "cb4-9" title = "9" > < span class = "co" > # 7 2009 0.01639344 NA NA 61 0.01639344 0.10130461< / span > < / a >
< a class = "sourceLine" id = "cb4-10" title = "10" > < span class = "co" > # 8 2010 0.05660377 NA NA 53 0.05660377 0.11182814< / span > < / a >
< a class = "sourceLine" id = "cb4-11" title = "11" > < span class = "co" > # 9 2011 0.18279570 NA NA 93 0.18279570 0.12329488< / span > < / a >
< a class = "sourceLine" id = "cb4-12" title = "12" > < span class = "co" > # 10 2012 0.30769231 NA NA 65 0.30769231 0.13575768< / span > < / a >
< a class = "sourceLine" id = "cb4-13" title = "13" > < span class = "co" > # 11 2013 0.06896552 NA NA 58 0.06896552 0.14926576< / span > < / a >
< a class = "sourceLine" id = "cb4-14" title = "14" > < span class = "co" > # 12 2014 0.10000000 NA NA 60 0.10000000 0.16386307< / span > < / a >
< a class = "sourceLine" id = "cb4-15" title = "15" > < span class = "co" > # 13 2015 0.23636364 NA NA 55 0.23636364 0.17958657< / span > < / a >
< a class = "sourceLine" id = "cb4-16" title = "16" > < span class = "co" > # 14 2016 0.22619048 NA NA 84 0.22619048 0.19646431< / span > < / a >
< a class = "sourceLine" id = "cb4-17" title = "17" > < span class = "co" > # 15 2017 0.16279070 NA NA 86 0.16279070 0.21451350< / span > < / a >
< a class = "sourceLine" id = "cb4-18" title = "18" > < span class = "co" > # 16 2018 0.23373852 0.2021578 0.2653193 NA NA 0.23373852< / span > < / a >
< a class = "sourceLine" id = "cb4-19" title = "19" > < span class = "co" > # 17 2019 0.25412909 0.2168525 0.2914057 NA NA 0.25412909< / span > < / a >
< a class = "sourceLine" id = "cb4-20" title = "20" > < span class = "co" > # 18 2020 0.27565854 0.2321869 0.3191302 NA NA 0.27565854< / span > < / a >
< a class = "sourceLine" id = "cb4-21" title = "21" > < span class = "co" > # 19 2021 0.29828252 0.2481942 0.3483709 NA NA 0.29828252< / span > < / a >
< a class = "sourceLine" id = "cb4-22" title = "22" > < span class = "co" > # 20 2022 0.32193804 0.2649008 0.3789753 NA NA 0.32193804< / span > < / a >
< a class = "sourceLine" id = "cb4-23" title = "23" > < span class = "co" > # 21 2023 0.34654311 0.2823269 0.4107593 NA NA 0.34654311< / span > < / a >
< a class = "sourceLine" id = "cb4-24" title = "24" > < span class = "co" > # 22 2024 0.37199700 0.3004860 0.4435080 NA NA 0.37199700< / span > < / a >
< a class = "sourceLine" id = "cb4-25" title = "25" > < span class = "co" > # 23 2025 0.39818127 0.3193839 0.4769787 NA NA 0.39818127< / span > < / a >
< a class = "sourceLine" id = "cb4-26" title = "26" > < span class = "co" > # 24 2026 0.42496142 0.3390173 0.5109056 NA NA 0.42496142< / span > < / a >
< a class = "sourceLine" id = "cb4-27" title = "27" > < span class = "co" > # 25 2027 0.45218939 0.3593720 0.5450068 NA NA 0.45218939< / span > < / a >
< a class = "sourceLine" id = "cb4-28" title = "28" > < span class = "co" > # 26 2028 0.47970658 0.3804212 0.5789920 NA NA 0.47970658< / span > < / a >
< a class = "sourceLine" id = "cb4-29" title = "29" > < span class = "co" > # 27 2029 0.50734745 0.4021241 0.6125708 NA NA 0.50734745< / span > < / a > < / code > < / 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 = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb5-1" title = "1" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/graphics/topics/plot" > plot< / a > < / span > (predict_TZP)< / a > < / code > < / 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 = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb6-1" title = "1" > < span class = "kw" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > (predict_TZP)< / a > < / code > < / pre > < / div >
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< 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" title = "1" > < / a >
< a class = "sourceLine" id = "cb7-2" title = "2" > < span class = "co" > # choose for error bars instead of a ribbon< / span > < / a >
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< a class = "sourceLine" id = "cb7-3" title = "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 >
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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 >
< p > Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. < / p >
< div class = "sourceCode" id = "cb8" > < pre class = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb8-1" title = "1" > septic_patients < span class = "op" > %> %< / span > < / a >
2019-02-12 15:38:31 +01:00
< a class = "sourceLine" id = "cb8-2" title = "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 = "op" > ==< / span > < span class = "st" > "Gram positive"< / span > ) < span class = "op" > %> %< / span > < / a >
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< a class = "sourceLine" id = "cb8-3" title = "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 = "op" > %> %< / span > < span class = "st" > < / span > < / a >
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< a class = "sourceLine" id = "cb8-4" title = "4" > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > ()< / a >
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< a class = "sourceLine" id = "cb8-5" title = "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 >
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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 default model 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://www.rdocumentation.org/packages/stats/topics/glm" > 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://www.rdocumentation.org/packages/stats/topics/glm" > 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://www.rdocumentation.org/packages/stats/topics/lm" > 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" title = "1" > septic_patients < span class = "op" > %> %< / span > < / a >
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< a class = "sourceLine" id = "cb9-2" title = "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 = "op" > ==< / span > < span class = "st" > "Gram positive"< / span > ) < span class = "op" > %> %< / span > < / a >
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< a class = "sourceLine" id = "cb9-3" title = "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 >
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< a class = "sourceLine" id = "cb9-4" title = "4" > < span class = "st" > < / span > < span class = "kw" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > ()< / a >
2019-05-20 12:00:18 +02:00
< a class = "sourceLine" id = "cb9-5" title = "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 >
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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 = "sourceCode r" > < code class = "sourceCode r" > < a class = "sourceLine" id = "cb10-1" title = "1" > model < -< span class = "st" > < / span > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/attributes" > attributes< / a > < / span > (predict_TZP)< span class = "op" > $< / span > model< / a >
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< a class = "sourceLine" id = "cb10-2" title = "2" > < / a >
< a class = "sourceLine" id = "cb10-3" title = "3" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/summary" > summary< / a > < / span > (model)< span class = "op" > $< / span > family< / a >
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< a class = "sourceLine" id = "cb10-4" title = "4" > < span class = "co" > # < / span > < / a >
< a class = "sourceLine" id = "cb10-5" title = "5" > < span class = "co" > # Family: binomial < / span > < / a >
< a class = "sourceLine" id = "cb10-6" title = "6" > < span class = "co" > # Link function: logit< / span > < / a >
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< a class = "sourceLine" id = "cb10-7" title = "7" > < / a >
< a class = "sourceLine" id = "cb10-8" title = "8" > < span class = "kw" > < a href = "https://www.rdocumentation.org/packages/base/topics/summary" > summary< / a > < / span > (model)< span class = "op" > $< / span > coefficients< / a >
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< a class = "sourceLine" id = "cb10-9" title = "9" > < span class = "co" > # Estimate Std. Error z value Pr(> |z|)< / span > < / a >
< a class = "sourceLine" id = "cb10-10" title = "10" > < span class = "co" > # (Intercept) -224.3987194 48.0335384 -4.671709 2.987038e-06< / span > < / a >
< a class = "sourceLine" id = "cb10-11" title = "11" > < span class = "co" > # year 0.1106102 0.0238753 4.632831 3.606990e-06< / span > < / a > < / code > < / pre > < / div >
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