mirror of https://github.com/msberends/AMR.git
433 lines
16 KiB
R
433 lines
16 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# AUTHORS #
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# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# LICENCE #
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# This program is free software; you can redistribute it and/or modify #
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# it under the terms of the GNU General Public License version 2.0, #
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# as published by the Free Software Foundation. #
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# #
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# This program is distributed in the hope that it will be useful, #
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# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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# GNU General Public License for more details. #
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# ==================================================================== #
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#' Resistance of isolates in data.frame
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#'
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#' \strong{NOTE: use \code{\link{rsi}} in dplyr functions like \code{\link[dplyr]{summarise}}.} \cr Calculate the percentage of S, SI, I, IR or R of a \code{data.frame} containing isolates.
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#' @param tbl \code{data.frame} containing columns with antibiotic interpretations.
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#' @param ab character vector with 1, 2 or 3 antibiotics that occur as column names in \code{tbl}, like \code{ab = c("amox", "amcl")}
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#' @param interpretation antimicrobial interpretation of which the portion must be calculated. Valid values are \code{"S"}, \code{"SI"}, \code{"I"}, \code{"IR"} or \code{"R"}.
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#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA} with a warning (when \code{warning = TRUE}).
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#' @param percent return output as percent (text), will else (at default) be a double
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#' @param info calculate the amount of available isolates and print it, like \code{n = 423}
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#' @param warning show a warning when the available amount of isolates is below \code{minimum}
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#' @details Remember that you should filter your table to let it contain \strong{only first isolates}!
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#' @keywords rsi antibiotics isolate isolates
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#' @return Double or, when \code{percent = TRUE}, a character.
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#' @export
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#' @importFrom dplyr %>% n_distinct filter filter_at pull vars all_vars any_vars
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#' @seealso \code{\link{rsi}} for the function that can be used with \code{\link[dplyr]{summarise}} directly.
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#' @examples
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#' \dontrun{
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#' rsi_df(tbl_with_bloodcultures, 'amcl')
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#'
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#' rsi_df(tbl_with_bloodcultures, c('amcl', 'gent'), interpretation = 'IR')
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#'
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#' library(dplyr)
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#' # calculate current empiric 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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#' rsi_df(ab = c("amox", "metr"))
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#' }
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rsi_df <- function(tbl,
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ab,
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interpretation = 'IR',
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minimum = 30,
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percent = FALSE,
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info = TRUE,
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warning = TRUE) {
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# in case tbl$interpretation already exists:
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interpretations_to_check <- paste(interpretation, collapse = "")
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# validate:
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if (min(grepl('^[a-z]{3,4}$', ab)) == 0 &
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min(grepl('^rsi[1-2]$', ab)) == 0) {
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for (i in 1:length(ab)) {
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ab[i] <- paste0('rsi', i)
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}
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}
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if (!grepl('^(S|SI|IS|I|IR|RI|R){1}$', interpretations_to_check)) {
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stop('Invalid `interpretation`; must be "S", "SI", "I", "IR", or "R".')
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}
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if ('is_ic' %in% colnames(tbl)) {
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if (n_distinct(tbl$is_ic) > 1 & warning == TRUE) {
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warning('Dataset contains isolates from the Intensive Care. Exclude them from proper epidemiological analysis.')
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}
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}
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# transform when checking for different results
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if (interpretations_to_check %in% c('SI', 'IS')) {
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for (i in 1:length(ab)) {
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lijst <- tbl[, ab[i]]
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if ('I' %in% lijst) {
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tbl[which(tbl[ab[i]] == 'I'), ][ab[i]] <- 'S'
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}
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}
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interpretations_to_check <- 'S'
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}
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if (interpretations_to_check %in% c('RI', 'IR')) {
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for (i in 1:length(ab)) {
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lijst <- tbl[, ab[i]]
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if ('I' %in% lijst) {
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tbl[which(tbl[ab[i]] == 'I'), ][ab[i]] <- 'R'
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}
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}
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interpretations_to_check <- 'R'
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}
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# get fraction
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if (length(ab) == 1) {
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numerator <- tbl %>%
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filter(pull(., ab[1]) == interpretations_to_check) %>%
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nrow()
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denominator <- tbl %>%
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filter(pull(., ab[1]) %in% c("S", "I", "R")) %>%
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nrow()
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} else if (length(ab) == 2) {
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numerator <- tbl %>%
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filter_at(vars(ab[1], ab[2]),
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any_vars(. == interpretations_to_check)) %>%
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filter_at(vars(ab[1], ab[2]),
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all_vars(. %in% c("S", "R", "I"))) %>%
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nrow()
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denominator <- tbl %>%
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filter_at(vars(ab[1], ab[2]),
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all_vars(. %in% c("S", "R", "I"))) %>%
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nrow()
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} else if (length(ab) == 3) {
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numerator <- tbl %>%
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filter_at(vars(ab[1], ab[2], ab[3]),
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any_vars(. == interpretations_to_check)) %>%
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filter_at(vars(ab[1], ab[2], ab[3]),
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all_vars(. %in% c("S", "R", "I"))) %>%
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nrow()
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denominator <- tbl %>%
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filter_at(vars(ab[1], ab[2], ab[3]),
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all_vars(. %in% c("S", "R", "I"))) %>%
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nrow()
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} else {
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stop('Maximum of 3 drugs allowed.')
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}
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# build text part
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if (info == TRUE) {
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cat('n =', denominator)
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info.txt1 <- percent(denominator / nrow(tbl))
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if (denominator == 0) {
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info.txt1 <- 'none'
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}
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info.txt2 <- gsub(',', ' and',
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ab %>%
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abname(tolower = TRUE) %>%
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toString(), fixed = TRUE)
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info.txt2 <- gsub('rsi1 and rsi2', 'these two drugs', info.txt2, fixed = TRUE)
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info.txt2 <- gsub('rsi1', 'this drug', info.txt2, fixed = TRUE)
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cat(paste0(' (of ', nrow(tbl), ' in total; ', info.txt1, ' tested on ', info.txt2, ')\n'))
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}
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# calculate and format
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y <- numerator / denominator
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if (percent == TRUE) {
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y <- percent(y)
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}
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if (denominator < minimum) {
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if (warning == TRUE) {
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warning(paste0('TOO FEW ISOLATES OF ', toString(ab), ' (n = ', denominator, ', n < ', minimum, '); NO RESULT.'))
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}
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y <- NA
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}
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# output
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y
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}
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#' Resistance of isolates
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#'
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#' This function can be used in \code{dplyr}s \code{\link[dplyr]{summarise}}, see \emph{Examples}. Calculate the percentage S, SI, I, IR or R of a vector of isolates.
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#' @param ab1,ab2 list with interpretations of an antibiotic
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#' @inheritParams rsi_df
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#' @details This function uses the \code{\link{rsi_df}} function internally.
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#' @keywords rsi antibiotics isolate isolates
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#' @return Double or, when \code{percent = TRUE}, a character.
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#' @export
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#' @examples
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#' \dontrun{
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#' tbl %>%
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#' group_by(hospital) %>%
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#' summarise(cipr = rsi(cipr))
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#'
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#' tbl %>%
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#' group_by(year, hospital) %>%
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#' summarise(
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#' isolates = n(),
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#' cipro = rsi(cipr %>% as.rsi(), percent = TRUE),
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#' amoxi = rsi(amox %>% as.rsi(), percent = TRUE))
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#'
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#' rsi(as.rsi(isolates$amox))
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#'
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#' rsi(as.rsi(isolates$amcl), interpretation = "S")
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#' }
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rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FALSE, info = FALSE, warning = FALSE) {
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ab1.name <- deparse(substitute(ab1))
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if (ab1.name %like% '.[$].') {
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ab1.name <- unlist(strsplit(ab1.name, "$", fixed = TRUE))
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ab1.name <- ab1.name[length(ab1.name)]
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}
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if (!ab1.name %like% '^[a-z]{3,4}$') {
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ab1.name <- 'rsi1'
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}
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ab2.name <- deparse(substitute(ab2))
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if (ab2.name %like% '.[$].') {
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ab2.name <- unlist(strsplit(ab2.name, "$", fixed = TRUE))
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ab2.name <- ab2.name[length(ab2.name)]
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}
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if (!ab2.name %like% '^[a-z]{3,4}$') {
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ab2.name <- 'rsi2'
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}
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interpretation <- paste(interpretation, collapse = "")
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tbl <- tibble(rsi1 = ab1, rsi2 = ab2)
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colnames(tbl) <- c(ab1.name, ab2.name)
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if (length(ab2) == 1) {
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return(rsi_df(tbl = tbl,
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ab = ab1.name,
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interpretation = interpretation,
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minimum = minimum,
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percent = percent,
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info = info,
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warning = warning))
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} else {
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if (length(ab1) != length(ab2)) {
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stop('`ab1` (n = ', length(ab1), ') and `ab2` (n = ', length(ab2), ') must be of same length.', call. = FALSE)
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}
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if (interpretation != 'S') {
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warning('`interpretation` is not set to S, albeit analysing a combination therapy.')
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}
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return(rsi_df(tbl = tbl,
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ab = c(ab1.name, ab2.name),
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interpretation = interpretation,
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minimum = minimum,
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percent = percent,
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info = info,
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warning = warning))
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}
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}
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#' Predict antimicrobial resistance
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#'
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#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
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#' @param tbl table that contains columns \code{col_ab} and \code{col_date}
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#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}), supports tidyverse-like quotation
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#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, supports tidyverse-like quotation
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#' @param year_max highest year to use in the prediction model, deafults to 15 years after today
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#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
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#' @param model the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).
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#' @param I_as_R treat \code{I} as \code{R}
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#' @param preserve_measurements overwrite predictions of years that are actually available in the data, with the original data. The standard errors of those years will be \code{NA}.
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#' @param info print textual analysis with the name and \code{\link{summary}} of the model.
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#' @return \code{data.frame} with columns \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
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#' @seealso \code{\link{lm}} \cr \code{\link{glm}}
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#' @export
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#' @importFrom dplyr %>% pull mutate group_by_at summarise filter
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#' @importFrom reshape2 dcast
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#' @examples
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#' \dontrun{
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#' # use it directly:
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#' rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
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#' col_ab = "amcl", col_date = "date")
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#'
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#' # or with dplyr so you can actually read it:
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#' library(dplyr)
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#' tbl %>%
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#' filter(first_isolate == TRUE,
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#' genus == "Haemophilus") %>%
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#' rsi_predict(amcl, date)
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#' }
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#'
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#'
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#' # real live example:
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#' library(dplyr)
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#' septic_patients %>%
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#' # get bacteria properties like genus and species
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#' left_join_microorganisms("bactid") %>%
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#' # calculate first isolates
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#' mutate(first_isolate =
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#' first_isolate(.,
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#' "date",
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#' "patient_id",
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#' "bactid",
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#' col_specimen = NA,
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#' col_icu = NA)) %>%
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#' # filter on first E. coli isolates
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#' filter(genus == "Escherichia",
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#' species == "coli",
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#' first_isolate == TRUE) %>%
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#' # predict resistance of cefotaxime for next years
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#' rsi_predict(col_ab = "cfot",
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#' col_date = "date",
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#' year_max = 2025,
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#' preserve_measurements = FALSE)
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#'
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rsi_predict <- function(tbl,
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col_ab,
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col_date,
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year_max = as.integer(format(as.Date(Sys.Date()), '%Y')) + 15,
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year_every = 1,
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model = 'binomial',
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I_as_R = TRUE,
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preserve_measurements = TRUE,
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info = TRUE) {
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if (nrow(tbl) == 0) {
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stop('This table does not contain any observations.')
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}
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if (!col_ab %in% colnames(tbl)) {
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stop('Column ', col_ab, ' not found.')
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}
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if (!col_date %in% colnames(tbl)) {
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stop('Column ', col_date, ' not found.')
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}
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if ('grouped_df' %in% class(tbl)) {
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# no grouped tibbles please, mutate will throw errors
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tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
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}
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if (I_as_R == TRUE) {
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tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
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}
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if (!all(tbl %>% pull(col_ab) %>% as.rsi() %in% c(NA, 'S', 'I', 'R'))) {
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stop('Column ', col_ab, ' must contain antimicrobial interpretations (S, I, R).')
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}
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year <- function(x) {
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if (all(grepl('^[0-9]{4}$', x))) {
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x
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} else {
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as.integer(format(as.Date(x), '%Y'))
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}
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}
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years_predict <- seq(from = min(year(tbl %>% pull(col_date))), to = year_max, by = year_every)
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df <- tbl %>%
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mutate(year = year(tbl %>% pull(col_date))) %>%
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group_by_at(c('year', col_ab)) %>%
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summarise(n())
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colnames(df) <- c('year', 'antibiotic', 'count')
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df <- df %>%
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reshape2::dcast(year ~ antibiotic, value.var = 'count')
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if (model %in% c('binomial', 'binom', 'logit')) {
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logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
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if (info == TRUE) {
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cat('\nLogistic regression model (logit) with binomial distribution')
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cat('\n------------------------------------------------------------\n')
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print(summary(logitmodel))
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}
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predictmodel <- stats::predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
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prediction <- predictmodel$fit
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se <- predictmodel$se.fit
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} else if (model == 'loglin') {
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loglinmodel <- with(df, glm(R ~ year, family = poisson))
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if (info == TRUE) {
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cat('\nLog-linear regression model (loglin) with poisson distribution')
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cat('\n--------------------------------------------------------------\n')
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print(summary(loglinmodel))
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}
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predictmodel <- stats::predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
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prediction <- predictmodel$fit
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se <- predictmodel$se.fit
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} else if (model %in% c('lin', 'linear')) {
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linmodel <- with(df, lm((R / (R + S)) ~ year))
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if (info == TRUE) {
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cat('\nLinear regression model')
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cat('\n-----------------------\n')
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print(summary(linmodel))
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}
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predictmodel <- stats::predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
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prediction <- predictmodel$fit
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se <- predictmodel$se.fit
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} else {
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stop('No valid model selected.')
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}
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# prepare the output dataframe
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prediction <- data.frame(year = years_predict, probR = prediction, stringsAsFactors = FALSE)
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prediction$se_min <- prediction$probR - se
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prediction$se_max <- prediction$probR + se
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if (model == 'loglin') {
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prediction$probR <- prediction$probR %>%
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format(scientific = FALSE) %>%
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as.integer()
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prediction$se_min <- prediction$se_min %>% as.integer()
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prediction$se_max <- prediction$se_max %>% as.integer()
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colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
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} else {
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prediction$se_max[which(prediction$se_max > 1)] <- 1
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}
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prediction$se_min[which(prediction$se_min < 0)] <- 0
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total <- prediction
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if (preserve_measurements == TRUE) {
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# geschatte data vervangen door gemeten data
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if (I_as_R == TRUE) {
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if (!'I' %in% colnames(df)) {
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df$I <- 0
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}
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df$probR <- df$R / rowSums(df[, c('R', 'S', 'I')])
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} else {
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df$probR <- df$R / rowSums(df[, c('R', 'S')])
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}
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measurements <- data.frame(year = df$year,
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probR = df$probR,
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se_min = NA,
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se_max = NA,
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stringsAsFactors = FALSE)
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colnames(measurements) <- colnames(prediction)
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prediction <- prediction %>% filter(!year %in% df$year)
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total <- rbind(measurements, prediction)
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}
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total
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}
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