The Ultimate Guide to the accrualPlot Package in R
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What is the accrualPlot package?
The ‘accrualPlot’ package in R is a specialized tool designed to assist with the tracking and prediction of participant accrual in clinical trials. It’s an essential resource for researchers, statisticians, and clinical trial coordinators who need to monitor recruitment progress and forecast when a trial might reach its intended sample size.
Clinical trials often involve the recruitment of participants over a period of time. Keeping track of this recruitment process and predicting when it will be completed can be challenging. The ‘accrualPlot’ package offers a solution by providing functions that allow users to create recruitment plots and make predictions based on these plots.
One of the key features of the ‘accrualPlot’ package is its ability to generate an accrual prediction plot using an accrual data frame and a target sample size. This prediction is based on a weighted linear regression model, which uses the accrual data frame as input. The resulting plot provides a visual representation of the recruitment progress, and the prediction function can help estimate when the target sample size will be reached.
In addition to generating plots and predictions, the ‘accrualPlot’ package can also aid in the creation of an accrual data frame. This data frame consists of counts of participants included per day, serving as the basis for the subsequent analysis and visualization.
Another valuable feature of the ‘accrualPlot’ package is its provision of diagnostic plots. These plots offer insights into the data distribution, thereby assisting users in understanding the underlying trends and patterns in the accrual process.
In conclusion, the ‘accrualPlot’ package in R is a robust tool for managing and predicting participant accrual in clinical trials. By offering functions for creating accrual data frames, generating recruitment plots, and making accrual predictions, it serves as a valuable aid for those involved in the planning and execution of clinical trials.
- Title: Accrual Plots and Predictions for Clinical Trials
- Description: Tracking accrual in clinical trials is important for trial success. If accrual is too slow, the trial will take too long and be too expensive. If accrual is much faster than expected, time sensitive tasks such as the writing of statistical analysis plans might need to be rushed. 'accrualPlot' provides functions to aid the tracking of accrual and predict when a trial will reach it's intended sample size.
- Author: Lukas Bütikofer [cre, aut], Alan G. Haynes [aut]
- Maintainer: Lukas Bütikofer
How to install the accrualPlot package?
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What package information should you know?
Key features
- Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify built-in datasets. To identify the datasets for the accrualPlot package, visit our database of R datasets.
- Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes for the accrualPlot package, you can visit our visit our database of R vignettes.
- Citation: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find citation information for the accrualPlot package, visit our database of R package citations.
Technical details
- License type: MIT + file LICENSE. For license details, visit the Open Source Initiative website.
- Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The accrualPlot package does not have compilation requirements.
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The accrualPlot package has the following required dependencies: lubridate, R (>= 2.10).
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The accrualPlot package has the following suggested dependencies: knitr, markdown, patchwork, rmarkdown, testthat, vdiffr.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The accrualPlot package does not use any external sources.
- Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The accrualPlot package has the following imported packages: dplyr, ggplot2, grid, magrittr, purrr, rlang.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The accrualPlot package has no enhancements.
How to get help with the accrualPlot package?
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("accrualPlot") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
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Package website: The accrualPlot package has a dedicated website. You can visit: https://github.com/CTU-Bern/accrualPlot, https://ctu-bern.github.io/accrualPlot/.
- Developer support: You can email Lukas Bütikofer . For contact information, visit our R community directory.
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Other package guides
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