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194 lines
7.6 KiB
R
194 lines
7.6 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/get_episode.R
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\name{get_episode}
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\alias{get_episode}
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\alias{is_new_episode}
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\title{Determine Clinical or Epidemic Episodes}
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\usage{
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get_episode(x, episode_days = NULL, case_free_days = NULL, ...)
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is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)
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}
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\arguments{
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\item{x}{vector of dates (class \code{Date} or \code{POSIXt}), will be sorted internally to determine episodes}
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\item{episode_days}{episode length in days to specify the time period after which a new episode begins, can also be less than a day or \code{Inf}, see \emph{Details}}
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\item{case_free_days}{(inter-epidemic) interval length in days after which a new episode will start, can also be less than a day or \code{Inf}, see \emph{Details}}
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\item{...}{ignored, only in place to allow future extensions}
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}
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\value{
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\itemize{
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\item \code{\link[=get_episode]{get_episode()}}: an \link{integer} vector
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\item \code{\link[=is_new_episode]{is_new_episode()}}: a \link{logical} vector
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}
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}
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\description{
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These functions determine which items in a vector can be considered (the start of) a new episode. This can be used to determine clinical episodes for any epidemiological analysis. The \code{\link[=get_episode]{get_episode()}} function returns the index number of the episode per group, while the \code{\link[=is_new_episode]{is_new_episode()}} function returns \code{TRUE} for every new \code{\link[=get_episode]{get_episode()}} index. Both absolute and relative episode determination are supported.
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}
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\details{
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Episodes can be determined in two ways: absolute and relative.
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\enumerate{
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\item Absolute
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This method uses \code{episode_days} to define an episode length in days, after which a new episode will start. A common use case in AMR data analysis is microbial epidemiology: episodes of \emph{S. aureus} bacteraemia in ICU patients for example. The episode length could then be 30 days, so that new \emph{S. aureus} isolates after an ICU episode of 30 days will be considered a different (or new) episode.
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Thus, this method counts \strong{since the start of the previous episode}.
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\item Relative
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This method uses \code{case_free_days} to quantify the duration of case-free days (the inter-epidemic interval), after which a new episode will start. A common use case is infectious disease epidemiology: episodes of norovirus outbreaks in a hospital for example. The case-free period could then be 14 days, so that new norovirus cases after that time will be considered a different (or new) episode.
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Thus, this methods counts \strong{since the last case in the previous episode}.
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}
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In a table:\tabular{ccc}{
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Date \tab Using \code{episode_days = 7} \tab Using \code{case_free_days = 7} \cr
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2023-01-01 \tab 1 \tab 1 \cr
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2023-01-02 \tab 1 \tab 1 \cr
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2023-01-05 \tab 1 \tab 1 \cr
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2023-01-08 \tab 2** \tab 1 \cr
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2023-02-21 \tab 3 \tab 2*** \cr
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2023-02-22 \tab 3 \tab 2 \cr
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2023-02-23 \tab 3 \tab 2 \cr
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2023-02-24 \tab 3 \tab 2 \cr
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2023-03-01 \tab 4 \tab 2 \cr
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}
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** This marks the start of a new episode, because 8 January 2023 is more than 7 days since the start of the previous episode (1 January 2023). \cr
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*** This marks the start of a new episode, because 21 January 2023 is more than 7 days since the last case in the previous episode (8 January 2023).
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Either \code{episode_days} or \code{case_free_days} must be provided in the function.
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\subsection{Difference between \code{get_episode()} and \code{is_new_episode()}}{
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The \code{\link[=get_episode]{get_episode()}} function returns the index number of the episode, so all cases/patients/isolates in the first episode will have the number 1, all cases/patients/isolates in the second episode will have the number 2, etc.
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The \code{\link[=is_new_episode]{is_new_episode()}} function on the other hand, returns \code{TRUE} for every new \code{\link[=get_episode]{get_episode()}} index.
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To specify, when setting \code{episode_days = 365} (using method 1 as explained above), this is how the two functions differ:\tabular{cccc}{
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patient \tab date \tab \code{get_episode()} \tab \code{is_new_episode()} \cr
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A \tab 2019-01-01 \tab 1 \tab TRUE \cr
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A \tab 2019-03-01 \tab 1 \tab FALSE \cr
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A \tab 2021-01-01 \tab 2 \tab TRUE \cr
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B \tab 2008-01-01 \tab 1 \tab TRUE \cr
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B \tab 2008-01-01 \tab 1 \tab FALSE \cr
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C \tab 2020-01-01 \tab 1 \tab TRUE \cr
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}
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}
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\subsection{Other}{
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The \code{\link[=first_isolate]{first_isolate()}} function is a wrapper around the \code{\link[=is_new_episode]{is_new_episode()}} function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods.
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The \code{dplyr} package is not required for these functions to work, but these episode functions do support \link[dplyr:group_by]{variable grouping} and work conveniently inside \code{dplyr} verbs such as \code{\link[dplyr:filter]{filter()}}, \code{\link[dplyr:mutate]{mutate()}} and \code{\link[dplyr:summarise]{summarise()}}.
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}
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}
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\examples{
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# difference between absolute and relative determination of episodes:
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x <- data.frame(dates = as.Date(c(
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"2021-01-01",
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"2021-01-02",
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"2021-01-05",
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"2021-01-08",
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"2021-02-21",
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"2021-02-22",
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"2021-02-23",
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"2021-02-24",
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"2021-03-01",
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"2021-03-01"
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)))
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x$absolute <- get_episode(x$dates, episode_days = 7)
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x$relative <- get_episode(x$dates, case_free_days = 7)
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x
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# `example_isolates` is a data set available in the AMR package.
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# See ?example_isolates
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df <- example_isolates[sample(seq_len(2000), size = 100), ]
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get_episode(df$date, episode_days = 60) # indices
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is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
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# filter on results from the third 60-day episode only, using base R
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df[which(get_episode(df$date, 60) == 3), ]
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# the functions also work for less than a day, e.g. to include one per hour:
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get_episode(
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c(
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Sys.time(),
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Sys.time() + 60 * 60
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),
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episode_days = 1 / 24
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)
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\donttest{
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if (require("dplyr")) {
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# is_new_episode() can also be used in dplyr verbs to determine patient
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# episodes based on any (combination of) grouping variables:
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df \%>\%
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mutate(condition = sample(
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x = c("A", "B", "C"),
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size = 100,
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replace = TRUE
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)) \%>\%
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group_by(patient, condition) \%>\%
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mutate(new_episode = is_new_episode(date, 365)) \%>\%
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select(patient, date, condition, new_episode) \%>\%
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arrange(patient, condition, date)
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}
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if (require("dplyr")) {
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df \%>\%
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group_by(ward, patient) \%>\%
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transmute(date,
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patient,
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new_index = get_episode(date, 60),
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new_logical = is_new_episode(date, 60)
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) \%>\%
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arrange(patient, ward, date)
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}
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if (require("dplyr")) {
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df \%>\%
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group_by(ward) \%>\%
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summarise(
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n_patients = n_distinct(patient),
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n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
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n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),
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n_episodes_30 = sum(is_new_episode(date, episode_days = 30))
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)
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}
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# grouping on patients and microorganisms leads to the same
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# results as first_isolate() when using 'episode-based':
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if (require("dplyr")) {
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x <- df \%>\%
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filter_first_isolate(
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include_unknown = TRUE,
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method = "episode-based"
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)
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y <- df \%>\%
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group_by(patient, mo) \%>\%
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filter(is_new_episode(date, 365)) \%>\%
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ungroup()
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identical(x, y)
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}
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# but is_new_episode() has a lot more flexibility than first_isolate(),
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# since you can now group on anything that seems relevant:
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if (require("dplyr")) {
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df \%>\%
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group_by(patient, mo, ward) \%>\%
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mutate(flag_episode = is_new_episode(date, 365)) \%>\%
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select(group_vars(.), flag_episode)
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}
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}
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}
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\seealso{
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\code{\link[=first_isolate]{first_isolate()}}
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}
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