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add confidence intervals (fixed #70), remove combine_IR

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dr. M.S. (Matthijs) Berends
2022-10-20 16:08:01 +02:00
parent 85e2fbe4a3
commit aa2c0639d0
19 changed files with 248 additions and 144 deletions
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56
man/proportion.Rd
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@ -5,6 +5,7 @@
\alias{resistance}
\alias{portion}
\alias{susceptibility}
\alias{rsi_confidence_interval}
\alias{proportion_R}
\alias{proportion_IR}
\alias{proportion_I}
@ -14,13 +15,23 @@
\alias{rsi_df}
\title{Calculate Microbial Resistance}
\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/}.
\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
\usage{
resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
rsi_confidence_interval(
...,
ab_result = "R",
minimum = 30,
as_percent = FALSE,
only_all_tested = FALSE,
confidence_level = 0.95,
side = "both"
)
proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
@ -38,7 +49,7 @@ proportion_df(
minimum = 30,
as_percent = FALSE,
combine_SI = TRUE,
combine_IR = FALSE
confidence_level = 0.95
)
rsi_df(
@ -48,7 +59,7 @@ rsi_df(
minimum = 30,
as_percent = FALSE,
combine_SI = TRUE,
combine_IR = FALSE
confidence_level = 0.95
)
}
\arguments{
@ -60,15 +71,19 @@ rsi_df(
\item{only_all_tested}{(for combination therapies, i.e. using more than one variable for \code{...}): a \link{logical} to indicate that isolates must be tested for all antibiotics, see section \emph{Combination Therapy} below}
\item{ab_result}{antibiotic results to test against, must be one of more values of "R", "S", "I"}
\item{confidence_level}{the confidence level for the returned confidence interval. For the calculation, the number of S or SI isolates, and R isolates are compared with the total number of available isolates with R, S, or I by using \code{\link[=binom.test]{binom.test()}}, i.e., the Clopper-Pearson method.}
\item{side}{the side of the confidence interval to return. Defaults to \code{"both"} for a length 2 vector, but can also be (abbreviated as) \code{"min"}/\code{"left"}/\code{"lower"}/\code{"less"} or \code{"max"}/\code{"right"}/\code{"higher"}/\code{"greater"}.}
\item{data}{a \link{data.frame} containing columns with class \code{\link{rsi}} (see \code{\link[=as.rsi]{as.rsi()}})}
\item{translate_ab}{a column name of the \link{antibiotics} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{ab_property()}}}
\item{language}{language of the returned text, defaults to system language (see \code{\link[=get_AMR_locale]{get_AMR_locale()}}) and can also be set with \code{getOption("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
\item{combine_SI}{a \link{logical} to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the argument \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is \code{TRUE}.}
\item{combine_IR}{a \link{logical} to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see argument \code{combine_SI}.}
\item{combine_SI}{a \link{logical} to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant), defaults to \code{TRUE}}
}
\value{
A \link{double} or, when \code{as_percent = TRUE}, a \link{character}.
@ -81,6 +96,8 @@ These functions can be used to calculate the (co-)resistance or susceptibility o
\details{
The function \code{\link[=resistance]{resistance()}} is equal to the function \code{\link[=proportion_R]{proportion_R()}}. The function \code{\link[=susceptibility]{susceptibility()}} is equal to the function \code{\link[=proportion_SI]{proportion_SI()}}.
Use \code{\link[=rsi_confidence_interval]{rsi_confidence_interval()}} to calculate the confidence interval, which relies on \code{\link[=binom.test]{binom.test()}}, i.e., the Clopper-Pearson method. This function returns a vector of length 2 at default for antimicrobial \emph{resistance}. Change the \code{side} argument to "left"/"min" or "right"/"max" to return a single value, and change the \code{ab_result} argument to e.g. \code{c("S", "I")} to test for antimicrobial \emph{susceptibility}, see Examples.
\strong{Remember that you should filter your data to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set.
These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{\link[=count]{count()}} functions to count isolates. The function \code{\link[=susceptibility]{susceptibility()}} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can influence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} argument).}
@ -144,8 +161,16 @@ This AMR package honours this (new) insight. Use \code{\link[=susceptibility]{su
# run ?example_isolates for more info.
# base R ------------------------------------------------------------
resistance(example_isolates$AMX) # determines \%R
susceptibility(example_isolates$AMX) # determines \%S+I
# determines \%R
resistance(example_isolates$AMX)
rsi_confidence_interval(example_isolates$AMX)
rsi_confidence_interval(example_isolates$AMX,
confidence_level = 0.975)
# determines \%S+I:
susceptibility(example_isolates$AMX)
rsi_confidence_interval(example_isolates$AMX,
ab_result = c("S", "I"))
# be more specific
proportion_S(example_isolates$AMX)
@ -157,12 +182,27 @@ proportion_R(example_isolates$AMX)
# dplyr -------------------------------------------------------------
\donttest{
if (require("dplyr")) {
example_isolates \%>\%
group_by(ward) \%>\%
summarise(
r = resistance(CIP),
n = n_rsi(CIP)
) # n_rsi works like n_distinct in dplyr, see ?n_rsi
}
if (require("dplyr")) {
example_isolates \%>\%
group_by(ward) \%>\%
summarise(
cipro_R = resistance(CIP),
ci_min = rsi_confidence_interval(CIP, side = "min"),
ci_max = rsi_confidence_interval(CIP, side = "max"),
)
}
if (require("dplyr")) {
example_isolates \%>\%
group_by(ward) \%>\%