In 10/2018 we published rpact 1.0.0, a comprehensive package that enables the simulation and analysis of confirmatory adaptive designs (incl. classical group sequential designs) with continuous, binary, and survival endpoint. This package can be downloaded per CRAN and stands under the "GNU Lesser General Public License" version 3. In rpact particularly, the methods described in the recent monograph of Wassmer and Brannath (published by Springer, 2016) are implemented and made available for the public.
In 05/2019 we published rpact 2.0.0 containing lots of new features, e.g.:
- Power calculation at given or adapted sample size for means, rates and survival data
- Sample size and power calculation for survival trials with piecewise accrual time and intensity
- Sample size and power calculation for survival trials with exponential survival time, piecewise exponential survival time and survival times that follow a Weibull distribution
- Simulation tool for survival trials; our simulator is very fast because it was implemented with C++. Adaptive event number recalculations based on conditional power can be assessed
- Simulation tool for designs with continuous and binary endpoints. Adaptive sample size recalculations based on conditional power can be assessed
- Comprehensive and unified tool for performing sample size calculation for fixed sample size design
- Enhanced plot functionalities
In 09/2020 we published rpact 3.0.0 containing lots of new features, e.g.:
- Simulation tools for multi-arm design testing means, rates, and hazard ratios
- Analysis tools for multi-arm design testing means, rates, and hazard ratios
- getSimulationRates: exact versions for testing a rate (one-sample case) and equality of rates (two-sample case)
- getDataset: multi-arm datasets for means, rates, and survival data
- Analysis of fixed designs
- Summary for most rpact result objects substantially improved and enhanced
- getEventProbabilities and getNumberOfSubjects: plot of result object
- Visual comparison of two designs: plot(design1, design2)
- Functions setOutputFormat and getOutputFormat implemented: definition of user defined output formats
- Enhanced plot functionalities
In 06/2021 we published rpact 3.1.0 containing lots of new features, e.g.:
- Analysis tools for enrichment design testing means, rates, and hazard ratios
- Automatic boundary recalculations during the trial for analysis with alpha spending approach, including under- and over-running
- The new function getObjectRCode (short: rcmd) returns the original R command which produced any rpact result object, including all dependencies
Further developments are planned for the near future. Specifically, a specific methodology for survival endpoints with adaptation based on surrogates will available.
The simulation based evaluation of operating characteristics of adaptive designs are becoming increasingly important, and the package addresses this issue. We developed these simulations for the most relevant types of endpoints (continuous, binary, and survival) and included the assessment of sample size reassessment strategies based on conditional power, of futility rules and other strategies. As adaptive strategies classical group sequential tests, combination tests (inverse normal, Fisher's combination test), and adaptive tests based on the conditional rejection probability (CRP) principle are available.
A comprehensive output in form of summaries, graphs and tables is provided.
For the analysis and execution of an adaptive trial, all methods provided by the simulation are available. Specific results of the adaptive methodology are also available, e.g., overall confidence intervals and p-values and conditional and predictive power assessments. The R package is fully integrated in R (i.e., no "stand alone" package) such that R specific data entry, transformations, and summary statistics can be utilized.
The R package rpact is a fully documented and validated product, including
- user requirements specification,
- functional specification,
- technical design specification,
- test plan,
- installation guides,
- user guides, and
- release notes.
The validation of the R package was done compliant to FDA/GxP guidelines and to the validation process of “Base R” and “Recommended Packages” as described in: “R: Regulatory Compliance and Validation Issues, A Guidance Document for the Use of R in Regulated Clinical Trial Environments” (The R Foundation for Statistical Computing, March, 2018). The validation documentation is customized and is licensed for exclusive use by our RPACT SLA customers. To get an idea of our validation approach, take a look to the table of contents of the rpact validation documentation.