mirror of https://github.com/msberends/AMR.git
136 lines
5.5 KiB
R
136 lines
5.5 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/resistance.R
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\name{resistance}
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\alias{resistance}
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\alias{susceptibility}
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\alias{n_rsi}
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\alias{rsi}
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\title{Calculate resistance of isolates}
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\usage{
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resistance(ab, include_I = TRUE, minimum = 30, as_percent = FALSE)
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susceptibility(ab1, ab2 = NULL, include_I = FALSE, minimum = 30,
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as_percent = FALSE)
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n_rsi(ab1, ab2 = NULL)
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rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
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as_percent = FALSE, info = FALSE, warning = TRUE)
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}
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\arguments{
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\item{ab, ab1, ab2}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
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\item{include_I}{logical to indicate whether antimicrobial interpretations of "I" should be included}
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\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}.}
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\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
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\item{interpretation}{antimicrobial interpretation}
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\item{info}{\emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}}
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\item{warning}{\emph{DEPRECATED} show a warning when the available amount of isolates is below \code{minimum}}
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}
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\value{
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Double or, when \code{as_percent = TRUE}, a character.
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}
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\description{
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These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
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}
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\details{
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\strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
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The functions \code{resistance}, \code{susceptibility} and \code{n_rsi} calculate using hybrid evaluation (i.e. using C++), which makes these functions 25-30 times faster than the old \code{rsi} function. This function is still available for backwards compatibility but is deprecated.
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\if{html}{
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\cr
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To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
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\out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
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To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
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\cr
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For two antibiotics:
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\out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
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\cr
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Theoretically for three antibiotics:
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\out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
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}
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}
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\examples{
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library(dplyr)
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(p = susceptibility(cipr),
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n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(cipro_p = susceptibility(cipr, as_percent = TRUE),
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cipro_n = n_rsi(cipr),
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genta_p = susceptibility(gent, as_percent = TRUE),
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genta_n = n_rsi(gent),
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combination_p = susceptibility(cipr, gent, as_percent = TRUE),
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combination_n = n_rsi(cipr, gent))
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# Calculate resistance
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resistance(septic_patients$amox)
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rsi(septic_patients$amox, interpretation = "IR") # deprecated
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# Or susceptibility
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susceptibility(septic_patients$amox)
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rsi(septic_patients$amox, interpretation = "S") # deprecated
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# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can see that combination therapy does a lot more than mono therapy:
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susceptibility(septic_patients$amcl) # p = 67.8\%
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n_rsi(septic_patients$amcl) # n = 1641
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susceptibility(septic_patients$gent) # p = 69.1\%
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n_rsi(septic_patients$gent) # n = 1863
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with(septic_patients,
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susceptibility(amcl, gent)) # p = 90.6\%
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with(septic_patients,
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n_rsi(amcl, gent)) # n = 1580
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\dontrun{
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# calculate current empiric combination therapy of Helicobacter gastritis:
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my_table \%>\%
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filter(first_isolate == TRUE,
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genus == "Helicobacter") \%>\%
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summarise(p = susceptibility(amox, metr), # amoxicillin with metronidazole
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n = n_rsi(amox, metr))
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# How fast is this hybrid evaluation in C++ compared to R?
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# In other words: how is the speed improvement of the new `resistance` compared to old `rsi`?
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library(microbenchmark)
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df <- septic_patients \%>\% group_by(hospital_id, bactid) # 317 groups with sizes 1 to 167
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microbenchmark(old_IR = df \%>\% summarise(p = rsi(amox, minimum = 0, interpretation = "IR")),
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new_IR = df \%>\% summarise(p = resistance(amox, minimum = 0)),
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old_S = df \%>\% summarise(p = rsi(amox, minimum = 0, interpretation = "S")),
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new_S = df \%>\% summarise(p = susceptibility(amox, minimum = 0)),
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times = 5,
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unit = "s")
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# Unit: seconds
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# expr min lq mean median uq max neval
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# old_IR 1.95600230 1.96096857 1.97981537 1.96823318 2.00645711 2.00741568 5
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# new_IR 0.06872808 0.06984932 0.07162866 0.06987306 0.07050094 0.07919192 5
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# old_S 1.68893579 1.69024888 1.72461867 1.69785934 1.70428796 1.84176137 5
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# new_S 0.06737037 0.06838167 0.07431906 0.07745364 0.07827224 0.08011738 5
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# The old function took roughly 2 seconds, the new ones take 0.07 seconds.
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}
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}
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\keyword{antibiotics}
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\keyword{isolate}
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\keyword{isolates}
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\keyword{resistance}
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\keyword{rsi_df}
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\keyword{susceptibility}
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