rpact version 4.0.0 available on CRAN

Mon Jun 3

  • All reference classes in the package have been replaced by R6 classes. This change brings significant advantages, including improved performance, more flexible and cleaner object-oriented programming, and enhanced encapsulation of methods and properties. The transition to R6 classes allows for more efficient memory management and faster execution, making the package more robust and scalable. Additionally, R6 classes provide a more intuitive and user-friendly interface for developers, facilitating the creation and maintenance of complex data structures and workflows.
  • Extension of the function `getPerformanceScore()` for sample size recalculation rules to the setting of binary endpoints according to Bokelmann et al. (2024)
  • The `getSimulationMultiArmMeans()`, `getSimulationMultiArmRates()`, and `getSimulationMultiArmSurvival()` functions now support an enhanced `selectArmsFunction` argument. Previously, only `effectVector` and `stage` were allowed as arguments. Now, users can optionally utilize additional arguments for more powerful custom function implementations, including `conditionalPower`, `conditionalCriticalValue`, `plannedSubjects/plannedEvents`, `allocationRatioPlanned`, `selectedArms`, `thetaH1` (for means and survival), `stDevH1` (for means), `overallEffects`, and for rates additionally: `piTreatmentsH1`, `piControlH1`, `overallRates`, and `overallRatesControl`.
  • Same as above for`getSimulationEnrichmentMeans()`, `getSimulationEnrichmentRates()`, and `getSimulationEnrichmentSurvival()`. Specifically, support for population selection with `selectPopulationsFunction` argument based on predictive/posterior probabilities added (see #32)
  • The `fetch()` and `obtain()` functions can be used to extract a single parameter from an rpact result object, which is useful for writing pipe-operator linked commands
  • Issues #25, #35, and #36 fixed
  • Minor improvements
  • See NEWS on CRAN (The Comprehensive R Archive Network) for details:

rpact version 3.5.0 available on CRAN

Fri Jan 26

The new functions getSampleSizeCounts() and getPowerCounts() can be used to perform sample size calculations and the assessment of test characteristics for clinical trials with negative binomial distributed count data. This is possible for fixed sample size and group sequential designs.

For the latter, the methodology described in Muetze et al. (2019) is implemented. These functions can also be used to perform blinded sample size reassessments according to Friede and Schmidli (2010).

See NEWS on CRAN (The Comprehensive R Archive Network) for details:

rpact version 3.4.0 available on CRAN

Thu Jul 20

The new rpact version includes many improvements and new features, e.g., the new function getPerformanceScore() calculates the conditional performance score, its sub-scores and components according to Herrmann et al. (2020) for a given simulation result from a two-stage design; see NEWS on CRAN (The Comprehensive R Archive Network) for details:

rpact version 3.3.4 available on CRAN

Mon Feb 27

The new rpact version includes many improvements, e.g., stage-wise allocation ratio definition for simulation means and rates; see NEWS on CRAN (The Comprehensive R Archive Network) for details:


Presentation for the U.S. Food and Drug Administration (FDA), March 3, 2022, 9:00am - 11:00am…

Mar -

Online Training Course for PPD, January 13, 2022.

Jan -




“rpact is by far the easiest to use.”
(Professor Daniel Lakens, Human-Technology Interaction Group, Eindhoven University of Technology, The Netherlands)



“We regularly use rpact for the design of group-sequential and adaptive trials at our company. The package is continuously evolving and includes state-of-the-art methods such as estimation of…



“[…] it is an incredibly accessible and useful tool for sequential analyses. […] I think your rpact package and shiny app might be a bit of a game-changer on this front, as it makes the required…