The Ultimate Guide to the BayesCTDesign Package in R
Explore valuable documentation and insights to make the most of the BayesCTDesign package in R. Get ready to unlock the full potential of the BayesCTDesign package!
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What is the BayesCTDesign package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
BayesCTDesign is an R package that provides tools for Bayesian design of clinical trials. It includes functions for specifying prior distributions, computing operating characteristics, and optimizing trial designs.
- Title: Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data
- Description: A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) .
- Author: Barry Eggleston [cre, aut], Doug Wilson [aut], Becky McNeil [aut], Joseph Ibrahim [aut], Diane Catellier [fnd, rth, aut]
- Maintainer: Barry Eggleston
How to install the BayesCTDesign package?
In this section, we’ll walk you through the process of installing and loading the BayesCTDesign package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.
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What package information should you know?
In this section, we’ll go over the technical aspects of the BayesCTDesign package.
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 BayesCTDesign 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 BayesCTDesign 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 BayesCTDesign package, visit our database of R package citations.
Technical details
- License type: GPL-3. 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 BayesCTDesign 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 BayesCTDesign package has the following required dependencies: R (>= 3.5.0).
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The BayesCTDesign package has no suggested dependencies.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The BayesCTDesign 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 BayesCTDesign package has the following imported packages: eha (>= 2.9.0), ggplot2 (>= 2.2.1), survival (>= 2.41-3), reshape2 (>= 1.4.3), stats (>= 3.5.0).
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The BayesCTDesign package has no enhancements.
How to get help with the BayesCTDesign package?
In this section, we’ll discuss a variety of available resources for getting help with the BayesCTDesign package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("BayesCTDesign") 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 BayesCTDesign package has a dedicated website. You can visit: https://github.com/begglest/BayesCTDesign.
- Developer support: You can email Barry Eggleston . For contact information, visit our R community directory.
Additional resources
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Other package guides
Explore our comprehensive guides for other R packages. These guides are valuable resources for accessing a wide range of information, making it easier to navigate R documentation in one place.
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