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There are several excellent resources available to provide both background on group sequential design methods and examples of planning and analyses using R:

Since group sequential design methodology can also implemented in information monitoring trials, users should understand the fundamentals of these designs.

Using impart

There are several vignettes built into impart: these include vignettes on information monitoring designs as well as group sequential designs. To see all available vignettes in impart, use the vignettes command:

vignette(package = "impart")
Title Item

Vignettes include example data and code to show you how to use impart for information monitoring and group sequential designs.

NOTE: impart is tested using the testthat package with a continuous integration workflow, and test coverage assessed using codecov. Vignettes currently cover the complete workflow for trials with a continuous outcome. Other vignettes on binary, ordinal, and time-to-event outcomes are under active development. Please check back to see if there have been updates to the impart software or documentation.

Citing impart

If you are using impart in practice, please cite our work. This helps us identify how our work impacts the practice of randomized trials.

citation(package = "impart")
#> To cite package 'impart' in publications use:
#> 
#>   Van Lancker K, Betz J, Rosenblum M (2024). _impart: Information
#>   Monitoring for Precision Adaptive Randomized Trials_. R package
#>   version 0.1.0, https://github.com/jbetz-jhu/impart,
#>   <https://jbetz-jhu.github.io/impart/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {impart: Information Monitoring for Precision Adaptive Randomized Trials},
#>     author = {Kelly {Van Lancker} and Josh Betz and Michael Rosenblum},
#>     year = {2024},
#>     note = {R package version 0.1.0, 
#> https://github.com/jbetz-jhu/impart},
#>     url = {https://jbetz-jhu.github.io/impart/},
#>   }