News/Updates

rpact version 4.1.0 available on CRAN

Wed Oct 9

New features

• The new function getSimulationCounts() can be used to perform power simulations for clinical trials with negative binomial distributed count data. The function returns the simulated power, stopping probabilities, conditional power, and expected sample size for testing mean rates for negative binomial distributed event numbers in the two treatment groups testing situation.
• The functions getDesignGroupSequential(), getDesignInverseNormal(), and getDesignFisher() now support the argument directionUpper to specify the direction of the alternative for one-sided testing early at the design phase, see enhancement #26
• getSampleSizeCounts() and getPowerCounts() output boundary values also on the treatment effect scale, see enhancement #40
• The fetch() and obtain() functions can be used to extract multiple parameters from an rpact result object and support various output formats

Improvements, issues, and changes

• Usage of pipe-operators improved
• Analysis progress messages are only displayed when R is used interactively
• Manual use of kable() for rpact result objects marked as deprecated, as the formatting and display will be handled automatically by rpact
• The order of all summary entries has been revised and optimized
• Minimum version of suggested package ggplot2 changed from 2.2.0 to 3.2.0
• Issues #41, #44, #46, and #47 fixed
• When analyzing with a two-sided test, an issue with the calculation of the conditional rejection probability was fixed
• Bug is fixed: directionUpper = FALSE has no influence in simulation for testing rates in one-sample situation

See NEWS on CRAN (The Comprehensive R Archive Network) for details: https://CRAN.R-project.org/package=rpact

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• All reference classes in the package have been replaced by R6 classes.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: https://CRAN.R-project.org/package=rpact

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: https://CRAN.R-project.org/package=rpact

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: https://CRAN.R-project.org/package=rpact