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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 >
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< h2 id = "needed-r-packages" > Needed R packages< a class = "anchor" aria-label = "anchor" href = "#needed-r-packages" > < / a >
< / h2 >
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< 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 >
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< 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 >
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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 data 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 = "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" > %> %< / 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" > < -< / span > < span class = "va" > example_isolates< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / 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 >
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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 > 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 >
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< div class = "sourceCode" id = "cb3" > < pre class = "downlit sourceCode r" >
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< code class = "sourceCode R" > < span > < span class = "va" > predict_TZP< / span > < / span >
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< span > < span class = "co" > # < span style = "color: #949494;" > # A tibble: 31 × < / span > < / span > < / span >
< span > < span class = "co" > # year value se_min se_max observations observed estimated< / span > < / span >
< span > < span class = "co" > # < span style = "color: #BCBCBC;" > *< / span > < span style = "color: #949494; font-style: italic;" > < dbl> < / span > < span style = "color: #949494; font-style: italic;" > < dbl> < / span > < span style = "color: #949494; font-style: italic;" > < dbl> < / span > < span style = "color: #949494; font-style: italic;" > < dbl> < / span > < span style = "color: #949494; font-style: italic;" > < int> < / span > < span style = "color: #949494; font-style: italic;" > < dbl> < / span > < span style = "color: #949494; font-style: italic;" > < dbl> < / span > < / span > < / span >
< span > < span class = "co" > # < 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" > # < 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" > # < 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" > # < 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" > # < 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" > # < 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" > # < 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" > # < 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" > # < 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" > # < 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" > # < span style = "color: #949494;" > # … with 21 more rows< / span > < / span > < / span > < / 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 = "cb4" > < pre class = "downlit sourceCode r" >
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< 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 >
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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 >
< 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 = "cb5" > < pre class = "downlit sourceCode r" >
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< code class = "sourceCode R" > < span > < span class = "fu" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_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 >
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< div class = "sourceCode" id = "cb6" > < pre class = "downlit sourceCode r" >
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< 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_rsi_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 >
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< p > Resistance is not easily predicted; if we look at vancomycin
resistance in Gram-positive bacteria, the spread (i.e.
is enormous:< / p >
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< div class = "sourceCode" id = "cb7" > < pre class = "downlit sourceCode r" >
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< 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" > %> %< / 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" > %> %< / a > < / span > < / span >
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< 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" > %> %< / a > < / span > < / span >
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< span > < span class = "fu" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < / span > < / code > < / pre > < / div >
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< p > < img src = "resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width = "720" > < / p >
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< 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 >
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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 >
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< code > "binomial"< / code > or < code > "binom"< / code > or
< code > "logit"< / code >
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< / 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 >
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< p > For the vancomycin resistance in Gram-positive bacteria, a linear
model might be more appropriate:< / p >
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< div class = "sourceCode" id = "cb8" > < pre class = "downlit sourceCode r" >
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< 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" > %> %< / 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" > %> %< / a > < / span > < / span >
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< 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" > %> %< / a > < / span > < / span >
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< span > < span class = "fu" > < a href = "../reference/resistance_predict.html" > ggplot_rsi_predict< / a > < / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < / span > < / code > < / pre > < / div >
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< p > < img src = "resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width = "720" > < / p >
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< p > The model itself is also available from the object, as an
< code > attribute< / code > :< / p >
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< div class = "sourceCode" id = "cb9" > < pre class = "downlit sourceCode r" >
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< code class = "sourceCode R" > < span > < span class = "va" > model< / span > < span class = "op" > < -< / 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" > # < / span > < / span >
< span > < span class = "co" > # Family: binomial < / span > < / span >
< span > < span class = "co" > # Link function: logit< / 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" > coefficients< / span > < / span >
< span > < span class = "co" > # Estimate Std. Error z value Pr(> |z|)< / span > < / span >
< span > < span class = "co" > # (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05< / span > < / span >
< span > < span class = "co" > # year 0.09883005 0.02295317 4.305725 1.664395e-05< / span > < / span > < / code > < / pre > < / div >
< / div >
< / div >
< / main > < aside class = "col-md-3" > < nav id = "toc" > < h2 > On this page< / h2 >
< / nav > < / aside >
< / div >
< footer > < div class = "pkgdown-footer-left" >
< p > < / p >
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< p > < code > AMR< / code > (for R). Free and open-source, licenced under the < a target = "_blank" href = "https://github.com/msberends/AMR/blob/main/LICENSE" class = "external-link" > GNU General Public License version 2.0 (GPL-2)< / a > .< br > Developed at the < a target = "_blank" href = "https://www.rug.nl" class = "external-link" > University of Groningen< / a > and < a target = "_blank" href = "https://www.umcg.nl" class = "external-link" > University Medical Center Groningen< / a > in The Netherlands.< / p >
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< div class = "pkgdown-footer-right" >
< p > < / p >
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< p > < a target = "_blank" href = "https://www.rug.nl" class = "external-link" > < img src = "https://github.com/msberends/AMR/raw/main/pkgdown/logos/logo_rug.svg" style = "max-width: 150px;" > < / a > < a target = "_blank" href = "https://www.umcg.nl" class = "external-link" > < img src = "https://github.com/msberends/AMR/raw/main/pkgdown/logos/logo_umcg.svg" style = "max-width: 150px;" > < / a > < / p >
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