accrualPlot

Accrual Plots and Predictions for Clinical Trials


Keywords
recruitment
License
MIT

Documentation

accrualPlot

Actions Status

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

Installation

accrualPlot can be installed from CRAN in the usual manner:

install.packages('accrualPlot')

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")

Note that 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")

Overview

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)

Acknowledgements

The package logo was created with ggplot2 and hexSticker with icons from Font Awesome (via the emojifont package).