- Assumed median survival time: get[SampleSize/Power/Simulation]Survival now support direct input of arguments ‘median1’ and ‘median2’
- Output of generic function ‘summary’ improved
- Plot type 5 of getPower[…] and getSimulation[…] objects improved
- Output of getSampleSizeSurvival with given maxNumberOfSubjects improved
- Output of get[SampleSize/Power]Survival for Kappa != 1 improved
- Assert function for minNumberOfSubjectsPerStage corrected for undefined conditionalPower
- Two-sided boundaries on effect scale in survival design improved
- Error in ‘summary’ for getDesign[…] fixed
- Other minor improvements

- Incorrect output of function ‘summary’ fixed for getSampleSize[…] and getPower[…]
- as.data.frame: default value of argument ‘niceColumnNamesEnabled’ changed from TRUE to FALSE

- Plot function for Fisher design implemented
- Generic function ‘summary’ implemented for getDesign[…], getSampleSize[…], getPower[…], and getSimulation[…] results: a simple boundary summary will be displayed

- Generic function as.data.frame improved for getDesign[…], getSampleSize[…], getPower[…], and getSimulation[…] results
- Ouput of getStageResults() improved
- Improvements for Shiny app compatibility and better Shiny app performance
- Repeated p-values are no longer calculated for typeOfDesign = “WToptimum”
- Piecewise suvival time improved for numeric definition: median and pi will not be calculated and displayed any longer
- Plot: legend title and tick mark positioning improved; optional arguments xlim and ylim implemented
- Sample size/power: usage of argument ‘twoSidedPower’ optimized
- Performance of function rpwexp/getPiecewiseExponentialRandomNumbers improved (special thanks to Marcel Wolbers for his example code)
- For group sequential designs a warning will be displayed if information rates from design not according to data information
- Format for output of standard deviation optimized

- Minor corrections in the inline help
- Labeling of lower and upper critical values (effect scale) reverted
- Simulation for Fisher’s combination test corrected
- Parameter minNumberOfAdditionalEventsPerStage renamed to minNumberOfEventsPerStage
- Parameter maxNumberOfAdditionalEventsPerStage renamed to maxNumberOfEventsPerStage
- Parameter minNumberOfAdditionalSubjectsPerStage renamed to minNumberOfSubjectsPerStage
- Parameter maxNumberOfAdditionalSubjectsPerStage renamed to maxNumberOfSubjectsPerStage
- Output of function getAccrualTime() improved
- Validation of arguments maxNumberOfIterations, allocation1, and allocation2 added: check for positive integer
- Function getSampleSizeSurvival improved: numeric search for accrualTime if followUpTime is given
- Default value improved for analysis tools: if no effect was specified for conditional power calculation, the observed effect is selected
- Fixed: function getDataset produced an error if only one log-rank value and one event was defined
- Number of subjects per treatment arm are provided in output of simulation survival if allocation ratio != 1
- Function getSimulationSurvival improved: first value of minNumberOfEventsPerStage and maxNumberOfEventsPerStage must be NA or equal to first value of plannedSubjects

- Function base::isFALSE replaced to guarantee R 3.4.x compatibility
- C++ compiler warning on r-devel-linux-x86_64-debian-clang system removed
- C++ compiler error on r-patched-solaris-x86 system fixed

- 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

- Fisher design, analysis of means or rates, conditional rejection probabilities (CRP): calculation issue fixed for stage > 2
- Call of getSampleSize[Means/Rates/Survival] without design argument implemented
- For all ‘set.seed’ calls ‘kind’ and ‘normal.kind’ were specified as follows: kind = “Mersenne-Twister”, normal.kind = “Inversion”
- Minor code optimizations, e.g. ‘return()’ replaced by ‘return(invisible())’ if reasonable
- Bug in ‘readDatasets’ fixed: variable names ‘group’ and ‘groups’ are now accepted
- “Overall reject per stage” and “Overall futility per stage” renamed to “Overall reject” and “Overall futility”, respectively (also variable names).
- Labels “events..” and “..patients..” consistently changed to “# events..” and “# patients…”, respectively.
- Output format for ‘allocationRatioPlanned’ specified
- Method ‘show’ of class ‘ParameterSet’ expanded: R Markdown output features implemented
- getSampleSizeSurvival(): argument ‘maxNumberOfPatients’ was renamed in ‘maxNumberOfSubjects’
- Result output, inline help and documentation: the word ‘patient’ was replaced by ‘subject’
- Variables ‘numberOfSubjectsGroup1’ and ‘numberOfSubjectsGroup2’ were renamed to ‘numberOfSubjects1’ and ‘numberOfSubjects1’
- Final p-values for two-sided test (group sequential, inverse normal, and Fisher combination test) available
- Upper and lower boundaries on effect scale for testing rates in two samples

- First release of rpact