library(rpact)
packageVersion("rpact") # version should be version 3.0 or laterHow to use R Generics with rpact
Utilities
This document provides examples that illustrate the usage of R generic functions (short: R generics) with rpact, e.g.,
as.data.frame or summary.
Creating examples of selected rpact result objects
First, load the rpact package
[1] '4.0.0'In the following we define different (typical) examples of rpact applications. The results are rpact objects which refer to examples for design specifications, power and sample size calculations, simulation results and the results of a data analysis with defined design and datasets.
designGroupSequential <- getDesignGroupSequential(
alpha = 0.05, kMax = 4,
sided = 1, typeOfDesign = "WT", deltaWT = 0.1
)
designFisher <- getDesignFisher(
kMax = 4, alpha = 0.025,
informationRates = c(0.2, 0.5, 0.8, 1), alpha0Vec = rep(0.4, 3)
)
designCharacteristics <- getDesignCharacteristics(designGroupSequential)
powerAndASN <- getPowerAndAverageSampleNumber(designGroupSequential, theta = 0.5, nMax = 40)
sampleSizeResults <- getSampleSizeMeans(designGroupSequential)
powerResults <- getPowerMeans(designGroupSequential, maxNumberOfSubjects = 100)
designSet <- getDesignSet(design = designGroupSequential, deltaWT = c(0.3, 0.4))
dataset <- getDataset(
n1 = c(22, 11, 22, 11),
n2 = c(22, 13, 22, 13),
means1 = c(1, 1.1, 1, 1),
means2 = c(1.4, 1.5, 3, 2.5),
stDevs1 = c(1, 2, 2, 1.3),
stDevs2 = c(1, 2, 2, 1.3)
)
analysisResults <- getAnalysisResults(designGroupSequential, dataset)How to use R generic functions with rpact objects
This section describes how the defined objects can be used and displayed. It is important to understand this is the way how typically R objects can be handled with. Particularly, we tried to make the summary() output (except for the technical developer summaries) “ready to use”, e.g., for a design report.
Get field names of the object
names(designGroupSequential) [1] "kMax" "alpha" "stages"
[4] "informationRates" "userAlphaSpending" "criticalValues"
[7] "stageLevels" "alphaSpent" "bindingFutility"
[10] "tolerance" "typeOfDesign" "beta"
[13] "deltaWT" "deltaPT1" "deltaPT0"
[16] "futilityBounds" "gammaA" "gammaB"
[19] "optimizationCriterion" "sided" "betaSpent"
[22] "typeBetaSpending" "userBetaSpending" "power"
[25] "twoSidedPower" "constantBoundsHP" "betaAdjustment"
[28] "delayedInformation" "decisionCriticalValues" "reversalProbabilities" Access data of a field
designGroupSequential$criticalValues[1] 3.069028 2.325888 1.977663 1.762694designGroupSequential[["criticalValues"]][1] 3.069028 2.325888 1.977663 1.762694Print object
print(designGroupSequential)**Design parameters and output of group sequential design**
**User defined parameters**
* *Type of design*: Wang & Tsiatis Delta class
* *Maximum number of stages*: 4
* *Stages*: 1, 2, 3, 4
* *Information rates*: 0.250, 0.500, 0.750, 1.000
* *Significance level*: 0.0500
* *Delta for Wang & Tsiatis Delta class*: 0.1
**Default parameters**
* *Type II error rate*: 0.2000
* *Two-sided power*: FALSE
* *Test*: one-sided
* *Tolerance*: 0.00000001
**Output**
* *Cumulative alpha spending*: 0.001074, 0.010527, 0.028206, 0.050000
* *Critical values*: 3.069, 2.326, 1.978, 1.763
* *Stage levels (one-sided)*: 0.001074, 0.010012, 0.023983, 0.038976 print(designSet)Trial design set with 3 designs
Design parameters and output of group sequential design:
User defined parameters:
Type of design : Wang & Tsiatis Delta class
Maximum number of stages : 4
Stages : 1, 2, 3, 4
Information rates : 0.250, 0.500, 0.750, 1.000
Significance level : 0.0500
Delta for Wang & Tsiatis Delta class : 0.1
Derived from user defined parameters: not available
Default parameters:
Type II error rate : 0.2000
Two-sided power : FALSE
Test : one-sided
Tolerance : 0.00000001
Output:
Cumulative alpha spending : 0.001074, 0.010527, 0.028206, 0.050000
Critical values : 3.069, 2.326, 1.978, 1.763
Stage levels (one-sided) : 0.001074, 0.010012, 0.023983, 0.038976
Design parameters and output of group sequential design:
User defined parameters:
Type of design : Wang & Tsiatis Delta class
Significance level : 0.0500
Delta for Wang & Tsiatis Delta class : 0.3
Derived from user defined parameters:
Maximum number of stages : 4
Stages : 1, 2, 3, 4
Default parameters:
Information rates : 0.250, 0.500, 0.750, 1.000
Type II error rate : 0.2000
Two-sided power : FALSE
Test : one-sided
Tolerance : 0.00000001
Output:
Cumulative alpha spending : 0.006916, 0.020169, 0.035108, 0.050000
Critical values : 2.462, 2.143, 1.976, 1.866
Stage levels (one-sided) : 0.006916, 0.016059, 0.024076, 0.031052
Design parameters and output of group sequential design:
User defined parameters:
Type of design : Wang & Tsiatis Delta class
Significance level : 0.0500
Delta for Wang & Tsiatis Delta class : 0.4
Derived from user defined parameters:
Maximum number of stages : 4
Stages : 1, 2, 3, 4
Default parameters:
Information rates : 0.250, 0.500, 0.750, 1.000
Type II error rate : 0.2000
Two-sided power : FALSE
Test : one-sided
Tolerance : 0.00000001
Output:
Cumulative alpha spending : 0.01250, 0.02626, 0.03879, 0.05000
Critical values : 2.241, 2.091, 2.008, 1.951
Stage levels (one-sided) : 0.01250, 0.01826, 0.02232, 0.02552 print(sampleSizeResults)**Design plan parameters and output for means**
**Design parameters**
* *Information rates*: 0.250, 0.500, 0.750, 1.000
* *Critical values*: 3.069, 2.326, 1.978, 1.763
* *Futility bounds (binding)*: -Inf, -Inf, -Inf
* *Cumulative alpha spending*: 0.001074, 0.010527, 0.028206, 0.050000
* *Local one-sided significance levels*: 0.001074, 0.010012, 0.023983, 0.038976
* *Significance level*: 0.0500
* *Type II error rate*: 0.2000
* *Test*: one-sided
**Default parameters**
* *Mean ratio*: FALSE
* *Theta H0*: 0
* *Normal approximation*: FALSE
* *Alternatives*: 0.2, 0.4, 0.6, 0.8, 1.0
* *Standard deviation*: 1
* *Treatment groups*: 2
* *Planned allocation ratio*: 1
**Sample size and output**
* *Maximum number of subjects*: 649.6, 163.5, 73.5, 42, 27.5
* *Maximum number of subjects (1)*: 324.8, 81.7, 36.7, 21, 13.7
* *Maximum number of subjects (2)*: 324.8, 81.7, 36.7, 21, 13.7
* *Number of subjects [1]*: 162.4, 40.9, 18.4, 10.5, 6.9
* *Number of subjects [2]*: 324.8, 81.7, 36.7, 21, 13.7
* *Number of subjects [3]*: 487.2, 122.6, 55.1, 31.5, 20.6
* *Number of subjects [4]*: 649.6, 163.5, 73.5, 42, 27.5
* *Reject per stage [1]*: 0.03625
* *Reject per stage [2]*: 0.26640
* *Reject per stage [3]*: 0.30072
* *Reject per stage [4]*: 0.19663
* *Early stop*: 0.6034
* *Expected number of subjects under H0*: 643.2, 161.9, 72.8, 41.6, 27.2
* *Expected number of subjects under H0/H1*: 602.5, 151.6, 68.2, 39, 25.5
* *Expected number of subjects under H1*: 496.6, 125, 56.2, 32.1, 21
* *Critical values (treatment effect scale) [1]*: 0.490, 1.029, 1.697, 2.678, 4.519
* *Critical values (treatment effect scale) [2]*: 0.259, 0.525, 0.804, 1.108, 1.452
* *Critical values (treatment effect scale) [3]*: 0.180, 0.361, 0.545, 0.735, 0.933
* *Critical values (treatment effect scale) [4]*: 0.139, 0.277, 0.417, 0.558, 0.701
**Legend**
* *(i)*: values of treatment arm i
* *[k]*: values at stage kprint(dataset)Dataset of means:
Stages : 1, 1, 2, 2, 3, 3, 4, 4
Treatment groups : 1, 2, 1, 2, 1, 2, 1, 2
Sample sizes : 22, 22, 11, 13, 22, 22, 11, 13
Means : 1.0, 1.4, 1.1, 1.5, 1.0, 3.0, 1.0, 2.5
Standard deviations : 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 1.3, 1.3
Calculated data:
Cumulative sample sizes : 22, 22, 33, 35, 55, 57, 66, 70
Cumulative means : 1.000, 1.400, 1.033, 1.437, 1.020, 2.040, 1.017, 2.126
Cumulative standard deviations : 1.000, 1.000, 1.381, 1.425, 1.639, 1.823, 1.579, 1.739 Print object (kable version)
kable(designGroupSequential)| typeOfDesign | kMax | stages | informationRates | alpha | beta | twoSidedPower | deltaWT | sided | tolerance | alphaSpent | criticalValues | stageLevels |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| WT | 4 | 1 | 0.25 | 0.05 | 0.2 | FALSE | 0.1 | 1 | 0 | 0.0010738 | 3.069028 | 0.0010738 |
| WT | 4 | 2 | 0.50 | 0.05 | 0.2 | FALSE | 0.1 | 1 | 0 | 0.0105269 | 2.325889 | 0.0100123 |
| WT | 4 | 3 | 0.75 | 0.05 | 0.2 | FALSE | 0.1 | 1 | 0 | 0.0282060 | 1.977663 | 0.0239833 |
| WT | 4 | 4 | 1.00 | 0.05 | 0.2 | FALSE | 0.1 | 1 | 0 | 0.0500000 | 1.762694 | 0.0389761 |
kable(designSet)| designNumber | typeOfDesign | kMax | stages | informationRates | alpha | beta | twoSidedPower | deltaWT | sided | tolerance | alphaSpent | criticalValues | stageLevels |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | WT | 4 | 1 | 0.25 | 0.05 | 0.2 | FALSE | 0.1 | 1 | 0 | 0.0010738 | 3.069028 | 0.0010738 |
| 1 | WT | 4 | 2 | 0.50 | 0.05 | 0.2 | FALSE | 0.1 | 1 | 0 | 0.0105269 | 2.325889 | 0.0100123 |
| 1 | WT | 4 | 3 | 0.75 | 0.05 | 0.2 | FALSE | 0.1 | 1 | 0 | 0.0282060 | 1.977663 | 0.0239833 |
| 1 | WT | 4 | 4 | 1.00 | 0.05 | 0.2 | FALSE | 0.1 | 1 | 0 | 0.0500000 | 1.762694 | 0.0389761 |
| 2 | WT | 4 | 1 | 0.25 | 0.05 | 0.2 | FALSE | 0.3 | 1 | 0 | 0.0069159 | 2.461604 | 0.0069159 |
| 2 | WT | 4 | 2 | 0.50 | 0.05 | 0.2 | FALSE | 0.3 | 1 | 0 | 0.0201692 | 2.142951 | 0.0160585 |
| 2 | WT | 4 | 3 | 0.75 | 0.05 | 0.2 | FALSE | 0.3 | 1 | 0 | 0.0351084 | 1.976032 | 0.0240756 |
| 2 | WT | 4 | 4 | 1.00 | 0.05 | 0.2 | FALSE | 0.3 | 1 | 0 | 0.0500000 | 1.865547 | 0.0310524 |
| 3 | WT | 4 | 1 | 0.25 | 0.05 | 0.2 | FALSE | 0.4 | 1 | 0 | 0.0125050 | 2.241250 | 0.0125050 |
| 3 | WT | 4 | 2 | 0.50 | 0.05 | 0.2 | FALSE | 0.4 | 1 | 0 | 0.0262606 | 2.091160 | 0.0182569 |
| 3 | WT | 4 | 3 | 0.75 | 0.05 | 0.2 | FALSE | 0.4 | 1 | 0 | 0.0387925 | 2.008067 | 0.0223181 |
| 3 | WT | 4 | 4 | 1.00 | 0.05 | 0.2 | FALSE | 0.4 | 1 | 0 | 0.0500000 | 1.951121 | 0.0255213 |
kable(sampleSizeResults)| stages | alternative | meanRatio | thetaH0 | normalApproximation | stDev | groups | allocationRatioPlanned | maxNumberOfSubjects | maxNumberOfSubjects1 | maxNumberOfSubjects2 | numberOfSubjects | rejectPerStage | earlyStop | expectedNumberOfSubjectsH0 | expectedNumberOfSubjectsH01 | expectedNumberOfSubjectsH1 | criticalValuesEffectScale |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.2 | FALSE | 0 | FALSE | 1 | 2 | 1 | 649.63926 | 324.81963 | 324.81963 | 162.409815 | 0.0362454 | 0.6033704 | 643.17426 | 602.51988 | 496.60668 | 0.4895811 |
| 1 | 0.4 | FALSE | 0 | FALSE | 1 | 2 | 1 | 163.49107 | 81.74554 | 81.74554 | 40.872769 | 0.0362454 | 0.6033704 | 161.86406 | 151.63280 | 124.97822 | 1.0285711 |
| 1 | 0.6 | FALSE | 0 | FALSE | 1 | 2 | 1 | 73.48452 | 36.74226 | 36.74226 | 18.371130 | 0.0362454 | 0.6033704 | 72.75323 | 68.15457 | 56.17411 | 1.6971635 |
| 1 | 0.8 | FALSE | 0 | FALSE | 1 | 2 | 1 | 42.00708 | 21.00354 | 21.00354 | 10.501771 | 0.0362454 | 0.6033704 | 41.58904 | 38.96024 | 32.11166 | 2.6783678 |
| 1 | 1.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | 27.46496 | 13.73248 | 13.73248 | 6.866239 | 0.0362454 | 0.6033704 | 27.19163 | 25.47288 | 20.99516 | 4.5192513 |
| 2 | 0.2 | FALSE | 0 | FALSE | 1 | 2 | 1 | 649.63926 | 324.81963 | 324.81963 | 324.819630 | 0.2664007 | 0.6033704 | 643.17426 | 602.51988 | 496.60668 | 0.2593931 |
| 2 | 0.4 | FALSE | 0 | FALSE | 1 | 2 | 1 | 163.49107 | 81.74554 | 81.74554 | 81.745537 | 0.2664007 | 0.6033704 | 161.86406 | 151.63280 | 124.97822 | 0.5250421 |
| 2 | 0.6 | FALSE | 0 | FALSE | 1 | 2 | 1 | 73.48452 | 36.74226 | 36.74226 | 36.742260 | 0.2664007 | 0.6033704 | 72.75323 | 68.15457 | 56.17411 | 0.8044378 |
| 2 | 0.8 | FALSE | 0 | FALSE | 1 | 2 | 1 | 42.00708 | 21.00354 | 21.00354 | 21.003541 | 0.2664007 | 0.6033704 | 41.58904 | 38.96024 | 32.11166 | 1.1079569 |
| 2 | 1.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | 27.46496 | 13.73248 | 13.73248 | 13.732478 | 0.2664007 | 0.6033704 | 27.19163 | 25.47288 | 20.99516 | 1.4515592 |
| 3 | 0.2 | FALSE | 0 | FALSE | 1 | 2 | 1 | 649.63926 | 324.81963 | 324.81963 | 487.229445 | 0.3007242 | 0.6033704 | 643.17426 | 602.51988 | 496.60668 | 0.1796453 |
| 3 | 0.4 | FALSE | 0 | FALSE | 1 | 2 | 1 | 163.49107 | 81.74554 | 81.74554 | 122.618306 | 0.3007242 | 0.6033704 | 161.86406 | 151.63280 | 124.97822 | 0.3608670 |
| 3 | 0.6 | FALSE | 0 | FALSE | 1 | 2 | 1 | 73.48452 | 36.74226 | 36.74226 | 55.113391 | 0.3007242 | 0.6033704 | 72.75323 | 68.15457 | 56.17411 | 0.5453877 |
| 3 | 0.8 | FALSE | 0 | FALSE | 1 | 2 | 1 | 42.00708 | 21.00354 | 21.00354 | 31.505312 | 0.3007242 | 0.6033704 | 41.58904 | 38.96024 | 32.11166 | 0.7352369 |
| 3 | 1.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | 27.46496 | 13.73248 | 13.73248 | 20.598717 | 0.3007242 | 0.6033704 | 27.19163 | 25.47288 | 20.99516 | 0.9329396 |
| 4 | 0.2 | FALSE | 0 | FALSE | 1 | 2 | 1 | 649.63926 | 324.81963 | 324.81963 | 649.639259 | 0.1966296 | 0.6033704 | 643.17426 | 602.51988 | 496.60668 | 0.1385351 |
| 4 | 0.4 | FALSE | 0 | FALSE | 1 | 2 | 1 | 163.49107 | 81.74554 | 81.74554 | 163.491074 | 0.1966296 | 0.6033704 | 161.86406 | 151.63280 | 124.97822 | 0.2774789 |
| 4 | 0.6 | FALSE | 0 | FALSE | 1 | 2 | 1 | 73.48452 | 36.74226 | 36.74226 | 73.484521 | 0.1966296 | 0.6033704 | 72.75323 | 68.15457 | 56.17411 | 0.4172455 |
| 4 | 0.8 | FALSE | 0 | FALSE | 1 | 2 | 1 | 42.00708 | 21.00354 | 21.00354 | 42.007082 | 0.1966296 | 0.6033704 | 41.58904 | 38.96024 | 32.11166 | 0.5582576 |
| 4 | 1.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | 27.46496 | 13.73248 | 13.73248 | 27.464956 | 0.1966296 | 0.6033704 | 27.19163 | 25.47288 | 20.99516 | 0.7009424 |
kable(dataset)| stages | groups | sampleSizes | means | stDevs | overallSampleSizes | overallMeans | overallStDevs |
|---|---|---|---|---|---|---|---|
| 1 | 1 | 22 | 1.0 | 1.0 | 22 | 1.000000 | 1.000000 |
| 1 | 2 | 22 | 1.4 | 1.0 | 22 | 1.400000 | 1.000000 |
| 2 | 1 | 11 | 1.1 | 2.0 | 33 | 1.033333 | 1.381500 |
| 2 | 2 | 13 | 1.5 | 2.0 | 35 | 1.437143 | 1.425418 |
| 3 | 1 | 22 | 1.0 | 2.0 | 55 | 1.020000 | 1.639151 |
| 3 | 2 | 22 | 3.0 | 2.0 | 57 | 2.040351 | 1.822857 |
| 4 | 1 | 11 | 1.0 | 1.3 | 66 | 1.016667 | 1.578664 |
| 4 | 2 | 13 | 2.5 | 1.3 | 70 | 2.125714 | 1.738706 |
Show a summary of the object
kable(summary(designFisher))Warning in is.na(parameterValues): is.na() auf Nicht-(Liste oder Vektor) des
Typs 'environment' angewendet| object | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| equalAlpha | 4 | 1 | 0.2 | 0.025 | 0.4 | TRUE | 1 | 0 | 0.0136664 | 0.0136664 | 0.0136664 | FALSE |
| equalAlpha | 4 | 2 | 0.5 | 0.025 | 0.4 | TRUE | 1 | 0 | 0.0205509 | 0.0008922 | 0.0136664 | FALSE |
| equalAlpha | 4 | 3 | 0.8 | 0.025 | 0.4 | TRUE | 1 | 0 | 0.0237206 | 0.0000964 | 0.0136664 | FALSE |
| equalAlpha | 4 | 4 | 1.0 | 0.025 | NA | TRUE | 1 | 0 | 0.0250000 | 0.0000215 | 0.0136664 | FALSE |
kable(summary(powerResults))Warning in is.na(parameterValues): is.na() auf Nicht-(Liste oder Vektor) des
Typs 'environment' angewendet| object | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.0 | 100 | 25 | 0.0500000 | 0.0010738 | 0.0282060 | 99.00483 | 1.3822841 |
| 1 | 0.2 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.2 | 100 | 25 | 0.2470591 | 0.0050487 | 0.1431061 | 94.95447 | 1.3822841 |
| 1 | 0.4 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.4 | 100 | 25 | 0.6139591 | 0.0189495 | 0.4136885 | 84.65788 | 1.3822841 |
| 1 | 0.6 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.6 | 100 | 25 | 0.8979191 | 0.0571183 | 0.7370338 | 69.73819 | 1.3822841 |
| 1 | 0.8 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.8 | 100 | 25 | 0.9878959 | 0.1394173 | 0.9317317 | 56.02829 | 1.3822841 |
| 1 | 1.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 1.0 | 100 | 25 | 0.9994029 | 0.2787807 | 0.9904819 | 46.19160 | 1.3822841 |
| 2 | 0.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.0 | 100 | 50 | 0.0500000 | 0.0094531 | 0.0282060 | 99.00483 | 0.6805418 |
| 2 | 0.2 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.2 | 100 | 50 | 0.2470591 | 0.0486179 | 0.1431061 | 94.95447 | 0.6805418 |
| 2 | 0.4 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.4 | 100 | 50 | 0.6139591 | 0.1620976 | 0.4136885 | 84.65788 | 0.6805418 |
| 2 | 0.6 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.6 | 100 | 50 | 0.8979191 | 0.3592021 | 0.7370338 | 69.73819 | 0.6805418 |
| 2 | 0.8 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.8 | 100 | 50 | 0.9878959 | 0.5483022 | 0.9317317 | 56.02829 | 0.6805418 |
| 2 | 1.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 1.0 | 100 | 50 | 0.9994029 | 0.6042925 | 0.9904819 | 46.19160 | 0.6805418 |
| 3 | 0.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.0 | 100 | 75 | 0.0500000 | 0.0176791 | 0.0282060 | 99.00483 | 0.4645319 |
| 3 | 0.2 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.2 | 100 | 75 | 0.2470591 | 0.0894395 | 0.1431061 | 94.95447 | 0.4645319 |
| 3 | 0.4 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.4 | 100 | 75 | 0.6139591 | 0.2326414 | 0.4136885 | 84.65788 | 0.4645319 |
| 3 | 0.6 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.6 | 100 | 75 | 0.8979191 | 0.3207134 | 0.7370338 | 69.73819 | 0.4645319 |
| 3 | 0.8 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.8 | 100 | 75 | 0.9878959 | 0.2440122 | 0.9317317 | 56.02829 | 0.4645319 |
| 3 | 1.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 1.0 | 100 | 75 | 0.9994029 | 0.1074086 | 0.9904819 | 46.19160 | 0.4645319 |
| 4 | 0.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.0 | 100 | 100 | 0.0500000 | 0.0217940 | 0.0282060 | 99.00483 | 0.3562713 |
| 4 | 0.2 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.2 | 100 | 100 | 0.2470591 | 0.1039530 | 0.1431061 | 94.95447 | 0.3562713 |
| 4 | 0.4 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.4 | 100 | 100 | 0.6139591 | 0.2002706 | 0.4136885 | 84.65788 | 0.3562713 |
| 4 | 0.6 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.6 | 100 | 100 | 0.8979191 | 0.1608853 | 0.7370338 | 69.73819 | 0.3562713 |
| 4 | 0.8 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 0.8 | 100 | 100 | 0.9878959 | 0.0561643 | 0.9317317 | 56.02829 | 0.3562713 |
| 4 | 1.0 | FALSE | 0 | FALSE | 1 | 2 | 1 | TRUE | 1.0 | 100 | 100 | 0.9994029 | 0.0089210 | 0.9904819 | 46.19160 | 0.3562713 |
kable(summary(analysisResults))Warning in is.na(parameterValues): is.na() auf Nicht-(Liste oder Vektor) des
Typs 'environment' angewendet| object | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| FALSE | TRUE | TRUE | 1.662998 | continue | 0.0028101 | -1.386144 | 0.5861439 | 0.499999 | NA | NA | NA | NA |
| FALSE | TRUE | TRUE | 1.662998 | continue | 0.0001227 | -1.216028 | 0.4084087 | 0.499999 | NA | NA | NA | NA |
| FALSE | TRUE | TRUE | 1.662998 | continue | 0.0000000 | -1.676258 | -0.3644440 | 0.499999 | NA | NA | NA | NA |
| FALSE | TRUE | TRUE | 1.662998 | accept | NA | -1.615872 | -0.6022231 | 0.499999 | 0.9999205 | -1.546886 | -0.608249 | -1.077568 |
Coerce object to data.frame: as.data.frame
as.data.frame(designGroupSequential) typeOfDesign kMax stages informationRates alpha beta twoSidedPower deltaWT
1 WT 4 1 0.25 0.05 0.2 FALSE 0.1
2 WT 4 2 0.50 0.05 0.2 FALSE 0.1
3 WT 4 3 0.75 0.05 0.2 FALSE 0.1
4 WT 4 4 1.00 0.05 0.2 FALSE 0.1
sided tolerance alphaSpent criticalValues stageLevels
1 1 0.00000001 0.001073781 3.069028 0.001073781
2 1 0.00000001 0.010526914 2.325888 0.010012250
3 1 0.00000001 0.028205994 1.977663 0.023983344
4 1 0.00000001 0.050000000 1.762694 0.038976069as.data.frame(dataset) stages groups sampleSizes means stDevs overallSampleSizes overallMeans
1 1 1 22 1.0 1.0 22 1.000000
2 1 2 22 1.4 1.0 22 1.400000
3 2 1 11 1.1 2.0 33 1.033333
4 2 2 13 1.5 2.0 35 1.437143
5 3 1 22 1.0 2.0 55 1.020000
6 3 2 22 3.0 2.0 57 2.040351
7 4 1 11 1.0 1.3 66 1.016667
8 4 2 13 2.5 1.3 70 2.125714
overallStDevs
1 1.000000
2 1.000000
3 1.381500
4 1.425418
5 1.639151
6 1.822857
7 1.578664
8 1.738706Coerce object to data.frame: as.data.frame with argument ‘niceColumnNamesEnabled = TRUE’
as.data.frame(designFisher, niceColumnNamesEnabled = TRUE) Method Maximum # stages Stage Information rate Significance level Alpha_0
1 equalAlpha 4 1 0.2 0.025 0.4
2 equalAlpha 4 2 0.5 0.025 0.4
3 equalAlpha 4 3 0.8 0.025 0.4
4 equalAlpha 4 4 1.0 0.025 NA
Binding futility Test Tolerance Cumulative alpha spending
1 TRUE 1 0.00000000000001 0.01366638
2 TRUE 1 0.00000000000001 0.02055086
3 TRUE 1 0.00000000000001 0.02372061
4 TRUE 1 0.00000000000001 0.02500000
Critical value Stage level Non stochastic curtailment
1 0.01366637982 0.01366638 FALSE
2 0.00089215382 0.01366638 FALSE
3 0.00009643023 0.01366638 FALSE
4 0.00002151406 0.01366638 FALSEas.data.frame(designCharacteristics, niceColumnNamesEnabled = TRUE) Inflation factor Stage Information Power Rejection probability under H1
1 1.04846 1 1.620541 0.03624538 0.03624538
2 1.04846 2 3.241081 0.30264612 0.26640074
3 1.04846 3 4.861622 0.60337036 0.30072424
4 1.04846 4 6.482163 0.80000000 0.19662964
Futility probability under H1 Ratio expected vs fixed sample size under H1
1 0 0.8014789
2 0 0.8014789
3 0 0.8014789
4 NA 0.8014789
Ratio expected vs fixed sample size under a value between H0 and H1
1 0.9724133
2 0.9724133
3 0.9724133
4 0.9724133
Ratio expected vs fixed sample size under H0
1 1.038026
2 1.038026
3 1.038026
4 1.038026as.data.frame(powerAndASN, niceColumnNamesEnabled = TRUE) Stage Effect Average sample size (ASN) Power Early stop Overall reject
1 1 0.5 26.7863 0.9266075 0.06839003 0.9266075
2 2 0.5 26.7863 0.9266075 0.39887080 0.9266075
3 3 0.5 26.7863 0.9266075 0.31845861 0.9266075
4 4 0.5 26.7863 0.9266075 NA 0.9266075
Reject per stage Overall futility Futility stop per stage
1 0.06839003 0.000000002898419 0.0000000009865876
2 0.39887080 0.000000002898419 0.0000000009758407
3 0.31845861 0.000000002898419 0.0000000009359906
4 0.14088808 0.000000002898419 NACoerce object to matrix: as.matrix
as.matrix(designGroupSequential) Stage Type of design Maximum # stages Information rate Significance level
"1" "WT" "4" "0.25" "0.05"
"2" "WT" "4" "0.50" "0.05"
"3" "WT" "4" "0.75" "0.05"
"4" "WT" "4" "1.00" "0.05"
Type II error rate Two-sided power Delta (Wang & Tsiatis) Test Tolerance
"0.2" "FALSE" "0.1" "1" "0.00000001"
"0.2" "FALSE" "0.1" "1" "0.00000001"
"0.2" "FALSE" "0.1" "1" "0.00000001"
"0.2" "FALSE" "0.1" "1" "0.00000001"
Cumulative alpha spending Critical value Stage level
"0.001073781" "3.069028" "0.001073781"
"0.010526914" "2.325888" "0.010012250"
"0.028205994" "1.977663" "0.023983344"
"0.050000000" "1.762694" "0.038976069"System: rpact 4.0.0, R version 4.3.3 (2024-02-29 ucrt), platform: x86_64-w64-mingw32
To cite R in publications use:
R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. To cite package ‘rpact’ in publications use:
Wassmer G, Pahlke F (2024). rpact: Confirmatory Adaptive Clinical Trial Design and Analysis. R package version 4.0.0, https://www.rpact.com, https://github.com/rpact-com/rpact, https://rpact-com.github.io/rpact/, https://www.rpact.org.