# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis # # # # SOURCE # # https://gitlab.com/msberends/AMR # # # # LICENCE # # (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) # # # # This R package is free software; you can freely use and distribute # # it for both personal and commercial purposes under the terms of the # # GNU General Public License version 2.0 (GNU GPL-2), as published by # # the Free Software Foundation. # # # # This R package was created for academic research and was publicly # # released in the hope that it will be useful, but it comes WITHOUT # # ANY WARRANTY OR LIABILITY. # # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # #' Calculate microbial resistance #' #' @description These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. #' #' \code{resistance()} should be used to calculate resistance, \code{susceptibility()} should be used to calculate susceptibility.\cr #' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples. #' @param minimum the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source. #' @param as_percent a logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}. #' @param only_all_tested (for combination therapies, i.e. using more than one variable for \code{...}) a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination therapy} below #' @param data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}}) #' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}} #' @inheritParams ab_property #' @param combine_SI a 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 parameter \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is \code{TRUE}. #' @param combine_IR a 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 parameter \code{combine_SI}. #' @inheritSection as.rsi Interpretation of S, I and R #' @details #' The function \code{resistance()} is equal to the function \code{proportion_R()}. The function \code{susceptibility()} is equal to the function \code{proportion_SI()}. #' #' \strong{Remember that you should filter your table to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link{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[AMR]{count}} functions to count isolates. The function \code{susceptibility()} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can infuence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} parameter).} #' #' The function \code{proportion_df()} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}()}) and calculates the proportions R, I and S. The function \code{rsi_df()} works exactly like \code{proportion_df()}, but adds the number of isolates. #' @section Combination therapy: #' When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{susceptibility} works to calculate the \%SI: #' #' \preformatted{ #' -------------------------------------------------------------------- #' only_all_tested = FALSE only_all_tested = TRUE #' ----------------------- ----------------------- #' Drug A Drug B include as include as include as include as #' numerator denominator numerator denominator #' -------- -------- ---------- ----------- ---------- ----------- #' S or I S or I X X X X #' R S or I X X X X #' S or I X X - - #' S or I R X X X X #' R R - X - X #' R - - - - #' S or I X X - - #' R - - - - #' - - - - #' -------------------------------------------------------------------- #' } #' #' Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that: #' \preformatted{ #' count_S() + count_I() + count_R() = count_all() #' proportion_S() + proportion_I() + proportion_R() = 1 #' } #' and that, in combination therapies, for \code{only_all_tested = FALSE} applies that: #' \preformatted{ #' count_S() + count_I() + count_R() >= count_all() #' proportion_S() + proportion_I() + proportion_R() >= 1 #' } #' #' Using \code{only_all_tested} has no impact when only using one antibiotic as input. #' @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/}. #' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptible isolates. #' @return Double or, when \code{as_percent = TRUE}, a character. #' @rdname proportion #' @aliases portion #' @name proportion #' @export #' @inheritSection AMR Read more on our website! #' @examples #' # example_isolates is a data set available in the AMR package. #' ?example_isolates #' #' resistance(example_isolates$AMX) # determines %R #' susceptibility(example_isolates$AMX) # determines %S+I #' #' # be more specific #' proportion_S(example_isolates$AMX) #' proportion_SI(example_isolates$AMX) #' proportion_I(example_isolates$AMX) #' proportion_IR(example_isolates$AMX) #' proportion_R(example_isolates$AMX) #' #' library(dplyr) #' example_isolates %>% #' group_by(hospital_id) %>% #' summarise(r = resistance(CIP), #' n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi #' #' example_isolates %>% #' group_by(hospital_id) %>% #' summarise(R = resistance(CIP, as_percent = TRUE), #' SI = susceptibility(CIP, as_percent = TRUE), #' n1 = count_all(CIP), # the actual total; sum of all three #' n2 = n_rsi(CIP), # same - analogous to n_distinct #' total = n()) # NOT the number of tested isolates! #' #' # Calculate co-resistance between amoxicillin/clav acid and gentamicin, #' # so we can see that combination therapy does a lot more than mono therapy: #' example_isolates %>% susceptibility(AMC) # %SI = 76.3% #' example_isolates %>% count_all(AMC) # n = 1879 #' #' example_isolates %>% susceptibility(GEN) # %SI = 75.4% #' example_isolates %>% count_all(GEN) # n = 1855 #' #' example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1% #' example_isolates %>% count_all(AMC, GEN) # n = 1939 #' #' #' # See Details on how `only_all_tested` works. Example: #' example_isolates %>% #' summarise(numerator = count_susceptible(AMC, GEN), #' denominator = count_all(AMC, GEN), #' proportion = susceptibility(AMC, GEN)) #' example_isolates %>% #' summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE), #' denominator = count_all(AMC, GEN, only_all_tested = TRUE), #' proportion = susceptibility(AMC, GEN, only_all_tested = TRUE)) #' #' #' example_isolates %>% #' group_by(hospital_id) %>% #' summarise(cipro_p = susceptibility(CIP, as_percent = TRUE), #' cipro_n = count_all(CIP), #' genta_p = susceptibility(GEN, as_percent = TRUE), #' genta_n = count_all(GEN), #' combination_p = susceptibility(CIP, GEN, as_percent = TRUE), #' combination_n = count_all(CIP, GEN)) #' #' # Get proportions S/I/R immediately of all rsi columns #' example_isolates %>% #' select(AMX, CIP) %>% #' proportion_df(translate = FALSE) #' #' # It also supports grouping variables #' example_isolates %>% #' select(hospital_id, AMX, CIP) %>% #' group_by(hospital_id) %>% #' proportion_df(translate = FALSE) #' #' #' \dontrun{ #' #' # calculate current empiric combination therapy of Helicobacter gastritis: #' my_table %>% #' filter(first_isolate == TRUE, #' genus == "Helicobacter") %>% #' summarise(p = susceptibility(AMX, MTR), # amoxicillin with metronidazole #' n = count_all(AMX, MTR)) #' } resistance <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = "R", minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname proportion #' @export susceptibility <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = c("S", "I"), minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname proportion #' @export proportion_R <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = "R", minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname proportion #' @export proportion_IR <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = c("I", "R"), minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname proportion #' @export proportion_I <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = "I", minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname proportion #' @export proportion_SI <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = c("S", "I"), minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname proportion #' @export proportion_S <- function(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) { rsi_calc(..., ab_result = "S", minimum = minimum, as_percent = as_percent, only_all_tested = only_all_tested, only_count = FALSE) } #' @rdname proportion #' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything #' @export proportion_df <- function(data, translate_ab = "name", language = get_locale(), minimum = 30, as_percent = FALSE, combine_SI = TRUE, combine_IR = FALSE) { rsi_calc_df(type = "proportion", data = data, translate_ab = translate_ab, language = language, minimum = minimum, as_percent = as_percent, combine_SI = combine_SI, combine_IR = combine_IR, combine_SI_missing = missing(combine_SI)) }