Accrual plots are an important tool when monitoring clinical trials. Some trials are terminated early due to low accrual, which is a waste of resources (including time). Assessing accrual rates can also be useful for planning analyses and estimating how long a trial needs to continue recruiting participants.
accrualPlot provides tools for such plots
accrualPlot can be installed from CRAN in the usual manner:
The development version of the package can be installed from the CTU Bern universe via
install.packages('accrualPlot', repos = 'https://ctu-bern.r-universe.dev')
accrualPlot can be installed directly from from github with:
# install.packages("remotes") remotes::install_github("CTU-Bern/accrualPlot")
remotes treats any warnings (e.g. that a certain package was built under a different version of R) as errors. If you see such an error, run the following line and try again:
Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS = "true")
The first step to using
accrualPlot is to create an accrual dataframe. This is simply a dataframe with a counts of participants included per day.
# load package library(accrualPlot) #> Loading required package: lubridate #> #> Attaching package: 'lubridate' #> The following objects are masked from 'package:base': #> #> date, intersect, setdiff, union # demonstration data data(accrualdemo) df <- accrual_create_df(accrualdemo$date)
Cumulative and absolute recruitment plots , as well as a method to predict the time point of study completion, are included.
par(mfrow = c(1,3)) plot(df, which = "cum") plot(df, which = "abs") plot(df, which = "pred", target = 300)
The package logo was created with
hexSticker with icons from Font Awesome (via the emojifont package).