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(v1.4.0.9025) is_new_episode()

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dr. M.S. (Matthijs) Berends
2020-11-23 21:50:27 +01:00
parent 363218da7e
commit b045b571a6
29 changed files with 706 additions and 366 deletions
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48
man/first_isolate.Rd
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@ -4,7 +4,6 @@
\alias{first_isolate}
\alias{filter_first_isolate}
\alias{filter_first_weighted_isolate}
\alias{is_new_episode}
\title{Determine first (weighted) isolates}
\source{
Methodology of this function is strictly based on:
@ -49,13 +48,6 @@ filter_first_weighted_isolate(
col_keyantibiotics = NULL,
...
)
is_new_episode(
.data,
episode_days = 365,
col_date = NULL,
col_patient_id = NULL
)
}
\arguments{
\item{x, .data}{a \link{data.frame} containing isolates.}
@ -98,10 +90,10 @@ is_new_episode(
A \code{\link{logical}} vector
}
\description{
Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. To determine patient episodes not necessarily based on microorganisms, use \code{\link[=is_new_episode]{is_new_episode()}} that also supports grouping with the \code{dplyr} package, see \emph{Examples}.
Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. To determine patient episodes not necessarily based on microorganisms, use \code{\link[=is_new_episode]{is_new_episode()}} that also supports grouping with the \code{dplyr} package.
}
\details{
The \code{\link[=is_new_episode]{is_new_episode()}} function is a wrapper around the \code{\link[=first_isolate]{first_isolate()}} function and can be used for data sets without isolates to just determine patient episodes based on any combination of grouping variables (using \code{dplyr}), please see \emph{Examples}. Since it runs \code{\link[=first_isolate]{first_isolate()}} for every group, it is quite slow.
The \code{\link[=first_isolate]{first_isolate()}} function is a wrapper around the \code{\link[=is_new_episode]{is_new_episode()}} function, but more efficient for data sets containing microorganism codes or names.
All isolates with a microbial ID of \code{NA} will be excluded as first isolate.
\subsection{Why this is so important}{
@ -191,42 +183,6 @@ if (require("dplyr")) {
# Gentamicin resistance in hospital D appears to be 3.7\% higher than
# when you (erroneously) would have used all isolates for analysis.
}
# filtering based on any other condition -----------------------------------
if (require("dplyr")) {
# is_new_episode() can be used in dplyr verbs to determine patient
# episodes based on any (combination of) grouping variables:
example_isolates \%>\%
mutate(condition = sample(x = c("A", "B", "C"),
size = 2000,
replace = TRUE)) \%>\%
group_by(condition) \%>\%
mutate(new_episode = is_new_episode())
example_isolates \%>\%
group_by(hospital_id) \%>\%
summarise(patients = n_distinct(patient_id),
n_episodes_365 = sum(is_new_episode(episode_days = 365)),
n_episodes_60 = sum(is_new_episode(episode_days = 60)),
n_episodes_30 = sum(is_new_episode(episode_days = 30)))
# grouping on microorganisms leads to the same results as first_isolate():
x <- example_isolates \%>\%
filter_first_isolate(include_unknown = TRUE)
y <- example_isolates \%>\%
group_by(mo) \%>\%
filter(is_new_episode())
identical(x$patient_id, y$patient_id)
# but now you can group on isolates and many more:
example_isolates \%>\%
group_by(mo, hospital_id, ward_icu) \%>\%
mutate(flag_episode = is_new_episode())
}
}
}
\seealso{