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new functions R, RI, SI, S

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dr. M.S. (Matthijs) Berends
2018-07-29 22:14:51 +02:00
parent 8421e3f005
commit 826694323b
10 changed files with 256 additions and 183 deletions
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2
man/first_isolate.Rd
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@ -4,7 +4,7 @@
\alias{first_isolate}
\title{Determine first (weighted) isolates}
\source{
Methodology of this function is based on: "M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition", 2014, Clinical and Laboratory Standards Institute. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
\usage{
first_isolate(tbl, col_date, col_patient_id, col_bactid = NA,

121
man/resistance.Rd
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@ -2,30 +2,47 @@
% Please edit documentation in R/resistance.R
\name{resistance}
\alias{resistance}
\alias{susceptibility}
\alias{S}
\alias{SI}
\alias{IR}
\alias{R}
\alias{n_rsi}
\alias{susceptibility}
\alias{rsi}
\title{Calculate resistance of isolates}
\source{
\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
\usage{
resistance(ab, include_I = TRUE, minimum = 30, as_percent = FALSE)
S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
IR(ab1, minimum = 30, as_percent = FALSE)
R(ab1, minimum = 30, as_percent = FALSE)
n_rsi(ab1, ab2 = NULL)
resistance(ab1, include_I = TRUE, minimum = 30, as_percent = FALSE)
susceptibility(ab1, ab2 = NULL, include_I = FALSE, minimum = 30,
as_percent = FALSE)
n_rsi(ab1, ab2 = NULL)
rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
as_percent = FALSE, info = FALSE, warning = TRUE)
}
\arguments{
\item{ab, ab1, ab2}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
\item{include_I}{logical to indicate whether antimicrobial interpretations of "I" should be included}
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a susceptible result. See Examples.}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}.}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
\item{include_I}{logical to indicate whether antimicrobial interpretations of "I" should be included}
\item{interpretation}{antimicrobial interpretation}
\item{info}{\emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}}
@ -36,14 +53,16 @@ rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
Double or, when \code{as_percent = TRUE}, a character.
}
\description{
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}.
These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. \cr\cr
\code{R} and \code{IR} can be used to calculate resistance, \code{S} and \code{SI} can be used to calculate susceptibility.\cr
\code{n_rsi} counts all cases where antimicrobial interpretations are available.
}
\details{
\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.
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.
The functions \code{resistance} and \code{susceptibility} are wrappers around \code{IR} and \code{S}, respectively. All functions except \code{rsi} use hybrid evaluation (i.e. using C++), which makes these functions 20-30 times faster than the old \code{rsi} function. This latter function is still available for backwards compatibility but is deprecated.
\if{html}{
\cr
\cr\cr
To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
\out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
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
@ -56,80 +75,60 @@ The functions \code{resistance}, \code{susceptibility} and \code{n_rsi} calculat
}
}
\examples{
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = susceptibility(cipr),
n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = susceptibility(cipr, as_percent = TRUE),
cipro_n = n_rsi(cipr),
genta_p = susceptibility(gent, as_percent = TRUE),
genta_n = n_rsi(gent),
combination_p = susceptibility(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent))
# Calculate resistance
resistance(septic_patients$amox)
rsi(septic_patients$amox, interpretation = "IR") # deprecated
R(septic_patients$amox)
IR(septic_patients$amox)
# Or susceptibility
susceptibility(septic_patients$amox)
rsi(septic_patients$amox, interpretation = "S") # deprecated
S(septic_patients$amox)
SI(septic_patients$amox)
# Since n_rsi counts available isolates (and is used as denominator),
# you can calculate back to e.g. count resistant isolates:
IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = S(cipr),
n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
susceptibility(septic_patients$amcl) # p = 67.8\%
n_rsi(septic_patients$amcl) # n = 1641
S(septic_patients$amcl) # p = 67.3\%
n_rsi(septic_patients$amcl) # n = 1570
susceptibility(septic_patients$gent) # p = 69.1\%
n_rsi(septic_patients$gent) # n = 1863
S(septic_patients$gent) # p = 74.0\%
n_rsi(septic_patients$gent) # n = 1842
with(septic_patients,
susceptibility(amcl, gent)) # p = 90.6\%
S(amcl, gent)) # p = 92.1\%
with(septic_patients,
n_rsi(amcl, gent)) # n = 1580
n_rsi(amcl, gent)) # n = 1504
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = S(cipr, as_percent = TRUE),
cipro_n = n_rsi(cipr),
genta_p = S(gent, as_percent = TRUE),
genta_n = n_rsi(gent),
combination_p = S(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent))
\dontrun{
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = susceptibility(amox, metr), # amoxicillin with metronidazole
summarise(p = S(amox, metr), # amoxicillin with metronidazole
n = n_rsi(amox, metr))
# How fast is this hybrid evaluation in C++ compared to R?
# In other words: how is the speed improvement of the new `resistance` compared to old `rsi`?
library(microbenchmark)
df <- septic_patients \%>\% group_by(hospital_id, bactid) # 317 groups with sizes 1 to 167
microbenchmark(old_IR = df \%>\% summarise(p = rsi(amox, minimum = 0, interpretation = "IR")),
new_IR = df \%>\% summarise(p = resistance(amox, minimum = 0)),
old_S = df \%>\% summarise(p = rsi(amox, minimum = 0, interpretation = "S")),
new_S = df \%>\% summarise(p = susceptibility(amox, minimum = 0)),
times = 5,
unit = "s")
# Unit: seconds
# expr min lq mean median uq max neval
# old_IR 1.95600230 1.96096857 1.97981537 1.96823318 2.00645711 2.00741568 5
# new_IR 0.06872808 0.06984932 0.07162866 0.06987306 0.07050094 0.07919192 5
# old_S 1.68893579 1.69024888 1.72461867 1.69785934 1.70428796 1.84176137 5
# new_S 0.06737037 0.06838167 0.07431906 0.07745364 0.07827224 0.08011738 5
# The old function took roughly 2 seconds, the new ones take 0.07 seconds.
}
}
\keyword{antibiotics}
\keyword{isolate}
\keyword{isolates}
\keyword{resistance}
\keyword{rsi}
\keyword{rsi_df}
\keyword{susceptibility}