How to Create Summaries with rpact

Utilities
This document provides many different examples that illustrate the usage of the R generic function summary with rpact. This is a technical vignette and is to be considered mainly as a comprehensive overview of the possible summaries in rpact.
Author

Friedrich Pahlke Gernot Wassmer

Published

February 16, 2024

Global options

First, load the rpact package

library(rpact)
packageVersion("rpact")
[1] '4.0.0'

The following options can be set globally:

rpact.summary.output.size: one of c(“small”, “medium”, “large”); defines how many details will be included into the summary; default is “large”, i.e., all available details are displayed.

rpact.summary.justify: one of c(“right”, “left”, “centre”); shall the values be right-justified (the default), left-justified or centered.

rpact.summary.intervalFormat: defines how intervals will be displayed in the summary, default is “[%s; %s]”.

rpact.summary.digits: defines how many digits are to be used for numeric values (default is 3).

rpact.summary.digits.probs: defines how many digits are to be used for numeric values (default is one more than value of rpact.summary.digits, i.e., 4).

rpact.summary.trim.zeroes: if TRUE (default) zeroes will always displayed as “0”, e.g. “0.000” will become “0”.

Examples

options("rpact.summary.output.size" = "small") # small, medium, large
options("rpact.summary.output.size" = "medium") # small, medium, large
options("rpact.summary.output.size" = "large") # small, medium, large

options("rpact.summary.intervalFormat" = "[%s; %s]")
options("rpact.summary.intervalFormat" = "%s - %s")
options("rpact.summary.justify" = "left")
options("rpact.summary.justify" = "centre")
options("rpact.summary.justify" = "right")

Design summaries

kable(summary(getDesignGroupSequential(
    beta = 0.05, typeOfDesign = "asKD", gammaA = 1,
    typeBetaSpending = "bsOF"
)))
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 NA
asKD 3 1 0.3333333 0.025 0.05 0.4259109 FALSE -0.9929167 FALSE 1 1 0 0.0083333 0.0006869 bsOF 2.393980 0.0083333
asKD 3 2 0.6666667 0.025 0.05 0.8091854 FALSE 0.9821879 FALSE 1 1 0 0.0166667 0.0163747 bsOF 2.293768 0.0109019
asKD 3 3 1.0000000 0.025 0.05 0.9500000 FALSE NA FALSE 1 1 0 0.0250000 0.0500000 bsOF 2.199939 0.0139056 
kable(summary(getDesignGroupSequential(kMax = 1)))
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
OF 1 0.025 0.2 FALSE FALSE 1 0 1.959964 0.025 
kable(summary(getDesignGroupSequential(kMax = 4, sided = 2)))
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
OF 4 1 0.25 0.025 0.2 FALSE 2 0 0.0000047 4.578711 0.0000023
OF 4 2 0.50 0.025 0.2 FALSE 2 0 0.0012072 3.237637 0.0006026
OF 4 3 0.75 0.025 0.2 FALSE 2 0 0.0086446 2.643520 0.0041024
OF 4 4 1.00 0.025 0.2 FALSE 2 0 0.0250000 2.289355 0.0110294 
kable(summary(getDesignGroupSequential(kMax = 4, sided = 2), digits = 0))
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
OF 4 1 0.25 0.025 0.2 FALSE 2 0 0.0000047 4.578711 0.0000023
OF 4 2 0.50 0.025 0.2 FALSE 2 0 0.0012072 3.237637 0.0006026
OF 4 3 0.75 0.025 0.2 FALSE 2 0 0.0086446 2.643520 0.0041024
OF 4 4 1.00 0.025 0.2 FALSE 2 0 0.0250000 2.289355 0.0110294 
kable(summary(getDesignGroupSequential(futilityBounds = c(-6, 0)), digits = 5))
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
OF 3 1 0.3333333 0.025 0.2 FALSE -Inf FALSE 1 0 0.0002592 3.471091 0.0002592
OF 3 2 0.6666667 0.025 0.2 FALSE 0 FALSE 1 0 0.0071601 2.454432 0.0070554
OF 3 3 1.0000000 0.025 0.2 FALSE NA FALSE 1 0 0.0250000 2.004036 0.0225331

Design plan summaries

Design plan summaries - means

kable(summary(getSampleSizeMeans(sided = 2, alternative = -0.5)))
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
1 -0.5 FALSE 0 FALSE 1 2 1 154.6208 77.31039 77.31039 -0.3641002 0.3641002 0.025 
kable(summary(getPowerMeans(
    sided = 1, alternative = c(-0.5, -0.3),
    maxNumberOfSubjects = 100, directionUpper = FALSE
)))
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
1 -0.5 FALSE 0 FALSE 1 2 1 FALSE -0.5 100 0.6968888 100 50 50 -0.3968935
1 -0.3 FALSE 0 FALSE 1 2 1 FALSE -0.3 100 0.3175171 100 50 50 -0.3968935 
kable(summary(getSampleSizeMeans(getDesignGroupSequential(futilityBounds = c(1, 2))), digits = 0))
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 NA NA NA NA NA
1 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 472.17597 0.0966651 0.1967516 0.1209400 0.8997917 552.71475 772.74288 888.92003 0.3217108 0.0921383 0.1586553
1 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 118.91882 0.0966651 0.1967516 0.1209400 0.8997917 139.20274 194.61743 223.87696 0.6548235 0.1841899 0.1586553
1 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 53.51305 0.0966651 0.1967516 0.1209400 0.8997917 62.64074 87.57715 100.74384 1.0126827 0.2760806 0.1586553
1 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 30.63527 0.0966651 0.1967516 0.1209400 0.8997917 35.86071 50.13637 57.67406 1.4130293 0.3677631 0.1586553
1 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 20.06145 0.0966651 0.1967516 0.1209400 0.8997917 23.48332 32.83171 37.76775 1.8816482 0.4592364 0.1586553
2 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 944.35194 0.6063750 0.1967516 0.0758117 0.8997917 552.71475 772.74288 888.92003 0.1600384 0.1303375 0.0227501
2 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 237.83764 0.6063750 0.1967516 0.0758117 0.8997917 139.20274 194.61743 223.87696 0.3206900 0.2607519 0.0227501
2 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 107.02609 0.6063750 0.1967516 0.0758117 0.8997917 62.64074 87.57715 100.74384 0.4825611 0.3913034 0.0227501
2 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 61.27055 0.6063750 0.1967516 0.0758117 0.8997917 35.86071 50.13637 57.67406 0.6462406 0.5220190 0.0227501
2 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 40.12290 0.6063750 0.1967516 0.0758117 0.8997917 23.48332 32.83171 37.76775 0.8122809 0.6528737 0.0227501
3 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 1416.52791 0.0969599 0.1967516 NA 0.8997917 552.71475 772.74288 888.92003 0.1065879 NA NA
3 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 356.75646 0.0969599 0.1967516 NA 0.8997917 139.20274 194.61743 223.87696 0.2129547 NA NA
3 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 160.53914 0.0969599 0.1967516 NA 0.8997917 62.64074 87.57715 100.74384 0.3188550 NA NA
3 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 91.90582 0.0969599 0.1967516 NA 0.8997917 35.86071 50.13637 57.67406 0.4239965 NA NA
3 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 60.18435 0.0969599 0.1967516 NA 0.8997917 23.48332 32.83171 37.76775 0.5280208 NA NA 
kable(summary(getPowerMeans(getDesignGroupSequential(futilityBounds = c(1, 2)),
    maxNumberOfSubjects = 100, alternative = 1
)))
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 NA NA NA NA
1 1 FALSE 0 FALSE 1 2 1 TRUE 1 100 33.33333 0.9562564 0.2699593 0.0435894 0.0315735 0.9717093 57.5586 1.3402426 0.3520269 0.1586553
2 1 FALSE 0 FALSE 1 2 1 TRUE 1 100 66.66667 0.9562564 0.6581607 0.0435894 0.0120159 0.9717093 57.5586 0.6179659 0.4995496 0.0227501
3 1 FALSE 0 FALSE 1 2 1 TRUE 1 100 100.00000 0.9562564 0.0281364 0.0435894 NA 0.9717093 57.5586 0.4060009 NA NA 
kable(summary(getSampleSizeMeans(getDesignGroupSequential(futilityBounds = c(1, 2))), digits = 3))
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 NA NA NA NA NA
1 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 472.17597 0.0966651 0.1967516 0.1209400 0.8997917 552.71475 772.74288 888.92003 0.3217108 0.0921383 0.1586553
1 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 118.91882 0.0966651 0.1967516 0.1209400 0.8997917 139.20274 194.61743 223.87696 0.6548235 0.1841899 0.1586553
1 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 53.51305 0.0966651 0.1967516 0.1209400 0.8997917 62.64074 87.57715 100.74384 1.0126827 0.2760806 0.1586553
1 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 30.63527 0.0966651 0.1967516 0.1209400 0.8997917 35.86071 50.13637 57.67406 1.4130293 0.3677631 0.1586553
1 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 20.06145 0.0966651 0.1967516 0.1209400 0.8997917 23.48332 32.83171 37.76775 1.8816482 0.4592364 0.1586553
2 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 944.35194 0.6063750 0.1967516 0.0758117 0.8997917 552.71475 772.74288 888.92003 0.1600384 0.1303375 0.0227501
2 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 237.83764 0.6063750 0.1967516 0.0758117 0.8997917 139.20274 194.61743 223.87696 0.3206900 0.2607519 0.0227501
2 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 107.02609 0.6063750 0.1967516 0.0758117 0.8997917 62.64074 87.57715 100.74384 0.4825611 0.3913034 0.0227501
2 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 61.27055 0.6063750 0.1967516 0.0758117 0.8997917 35.86071 50.13637 57.67406 0.6462406 0.5220190 0.0227501
2 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 40.12290 0.6063750 0.1967516 0.0758117 0.8997917 23.48332 32.83171 37.76775 0.8122809 0.6528737 0.0227501
3 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 1416.52791 0.0969599 0.1967516 NA 0.8997917 552.71475 772.74288 888.92003 0.1065879 NA NA
3 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 356.75646 0.0969599 0.1967516 NA 0.8997917 139.20274 194.61743 223.87696 0.2129547 NA NA
3 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 160.53914 0.0969599 0.1967516 NA 0.8997917 62.64074 87.57715 100.74384 0.3188550 NA NA
3 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 91.90582 0.0969599 0.1967516 NA 0.8997917 35.86071 50.13637 57.67406 0.4239965 NA NA
3 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 60.18435 0.0969599 0.1967516 NA 0.8997917 23.48332 32.83171 37.76775 0.5280208 NA NA 
kable(summary(getSampleSizeMeans(getDesignGroupSequential(futilityBounds = c(1, 2))), digits = 0))
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 NA NA NA NA NA
1 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 472.17597 0.0966651 0.1967516 0.1209400 0.8997917 552.71475 772.74288 888.92003 0.3217108 0.0921383 0.1586553
1 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 118.91882 0.0966651 0.1967516 0.1209400 0.8997917 139.20274 194.61743 223.87696 0.6548235 0.1841899 0.1586553
1 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 53.51305 0.0966651 0.1967516 0.1209400 0.8997917 62.64074 87.57715 100.74384 1.0126827 0.2760806 0.1586553
1 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 30.63527 0.0966651 0.1967516 0.1209400 0.8997917 35.86071 50.13637 57.67406 1.4130293 0.3677631 0.1586553
1 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 20.06145 0.0966651 0.1967516 0.1209400 0.8997917 23.48332 32.83171 37.76775 1.8816482 0.4592364 0.1586553
2 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 944.35194 0.6063750 0.1967516 0.0758117 0.8997917 552.71475 772.74288 888.92003 0.1600384 0.1303375 0.0227501
2 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 237.83764 0.6063750 0.1967516 0.0758117 0.8997917 139.20274 194.61743 223.87696 0.3206900 0.2607519 0.0227501
2 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 107.02609 0.6063750 0.1967516 0.0758117 0.8997917 62.64074 87.57715 100.74384 0.4825611 0.3913034 0.0227501
2 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 61.27055 0.6063750 0.1967516 0.0758117 0.8997917 35.86071 50.13637 57.67406 0.6462406 0.5220190 0.0227501
2 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 40.12290 0.6063750 0.1967516 0.0758117 0.8997917 23.48332 32.83171 37.76775 0.8122809 0.6528737 0.0227501
3 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 1416.52791 0.0969599 0.1967516 NA 0.8997917 552.71475 772.74288 888.92003 0.1065879 NA NA
3 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 356.75646 0.0969599 0.1967516 NA 0.8997917 139.20274 194.61743 223.87696 0.2129547 NA NA
3 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 160.53914 0.0969599 0.1967516 NA 0.8997917 62.64074 87.57715 100.74384 0.3188550 NA NA
3 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 91.90582 0.0969599 0.1967516 NA 0.8997917 35.86071 50.13637 57.67406 0.4239965 NA NA
3 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 60.18435 0.0969599 0.1967516 NA 0.8997917 23.48332 32.83171 37.76775 0.5280208 NA NA 
kable(summary(getSampleSizeMeans(getDesignGroupSequential(futilityBounds = c(1, 2))), digits = -1))
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 NA NA NA NA NA
1 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 472.17597 0.0966651 0.1967516 0.1209400 0.8997917 552.71475 772.74288 888.92003 0.3217108 0.0921383 0.1586553
1 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 118.91882 0.0966651 0.1967516 0.1209400 0.8997917 139.20274 194.61743 223.87696 0.6548235 0.1841899 0.1586553
1 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 53.51305 0.0966651 0.1967516 0.1209400 0.8997917 62.64074 87.57715 100.74384 1.0126827 0.2760806 0.1586553
1 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 30.63527 0.0966651 0.1967516 0.1209400 0.8997917 35.86071 50.13637 57.67406 1.4130293 0.3677631 0.1586553
1 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 20.06145 0.0966651 0.1967516 0.1209400 0.8997917 23.48332 32.83171 37.76775 1.8816482 0.4592364 0.1586553
2 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 944.35194 0.6063750 0.1967516 0.0758117 0.8997917 552.71475 772.74288 888.92003 0.1600384 0.1303375 0.0227501
2 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 237.83764 0.6063750 0.1967516 0.0758117 0.8997917 139.20274 194.61743 223.87696 0.3206900 0.2607519 0.0227501
2 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 107.02609 0.6063750 0.1967516 0.0758117 0.8997917 62.64074 87.57715 100.74384 0.4825611 0.3913034 0.0227501
2 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 61.27055 0.6063750 0.1967516 0.0758117 0.8997917 35.86071 50.13637 57.67406 0.6462406 0.5220190 0.0227501
2 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 40.12290 0.6063750 0.1967516 0.0758117 0.8997917 23.48332 32.83171 37.76775 0.8122809 0.6528737 0.0227501
3 0.2 FALSE 0 FALSE 1 2 1 1416.52791 708.26396 708.26396 1416.52791 0.0969599 0.1967516 NA 0.8997917 552.71475 772.74288 888.92003 0.1065879 NA NA
3 0.4 FALSE 0 FALSE 1 2 1 356.75646 178.37823 178.37823 356.75646 0.0969599 0.1967516 NA 0.8997917 139.20274 194.61743 223.87696 0.2129547 NA NA
3 0.6 FALSE 0 FALSE 1 2 1 160.53914 80.26957 80.26957 160.53914 0.0969599 0.1967516 NA 0.8997917 62.64074 87.57715 100.74384 0.3188550 NA NA
3 0.8 FALSE 0 FALSE 1 2 1 91.90582 45.95291 45.95291 91.90582 0.0969599 0.1967516 NA 0.8997917 35.86071 50.13637 57.67406 0.4239965 NA NA
3 1.0 FALSE 0 FALSE 1 2 1 60.18435 30.09218 30.09218 60.18435 0.0969599 0.1967516 NA 0.8997917 23.48332 32.83171 37.76775 0.5280208 NA NA

Design plan summaries - rates

kable(summary(getSampleSizeRates(pi2 = 0.3)))
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
1 0.4 FALSE 0 TRUE 0.3 2 1 TRUE 711.88563 355.94281 355.94281 0.0693262
1 0.5 FALSE 0 TRUE 0.3 2 1 TRUE 185.99769 92.99884 92.99884 0.1387203
1 0.6 FALSE 0 TRUE 0.3 2 1 TRUE 83.94051 41.97025 41.97025 0.2100298 
kable(summary(getSampleSizeRates(getDesignGroupSequential(futilityBounds = c(1, 2)))))
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 NA NA NA NA NA NA
1 0.4 FALSE 0 TRUE 0.2 2 1 TRUE 292.46176 146.23088 146.23088 97.48725 0.0966651 0.1967516 0.1209400 0.8997917 114.11560 159.54345 183.52982 0.3394075 0.0869341 0.1586553
1 0.5 FALSE 0 TRUE 0.2 2 1 TRUE 138.55397 69.27699 69.27699 46.18466 0.0966651 0.1967516 0.1209400 0.8997917 54.06235 75.58382 86.94739 0.5086236 0.1298716 0.1586553
1 0.6 FALSE 0 TRUE 0.2 2 1 TRUE 80.40347 40.20173 40.20173 26.80116 0.0966651 0.1967516 0.1209400 0.8997917 31.37261 43.86162 50.45594 0.6688923 0.1748346 0.1586553
2 0.4 FALSE 0 TRUE 0.2 2 1 TRUE 292.46176 146.23088 146.23088 194.97451 0.6063750 0.1967516 0.0758117 0.8997917 114.11560 159.54345 183.52982 0.1576438 0.1261296 0.0227501
2 0.5 FALSE 0 TRUE 0.2 2 1 TRUE 138.55397 69.27699 69.27699 92.36931 0.6063750 0.1967516 0.0758117 0.8997917 54.06235 75.58382 86.94739 0.2380664 0.1897820 0.0227501
2 0.6 FALSE 0 TRUE 0.2 2 1 TRUE 80.40347 40.20173 40.20173 53.60231 0.6063750 0.1967516 0.0758117 0.8997917 31.37261 43.86162 50.45594 0.3220308 0.2565571 0.0227501
3 0.4 FALSE 0 TRUE 0.2 2 1 TRUE 292.46176 146.23088 146.23088 292.46176 0.0969599 0.1967516 NA 0.8997917 114.11560 159.54345 183.52982 0.1015924 NA NA
3 0.5 FALSE 0 TRUE 0.2 2 1 TRUE 138.55397 69.27699 69.27699 138.55397 0.0969599 0.1967516 NA 0.8997917 54.06235 75.58382 86.94739 0.1522321 NA NA
3 0.6 FALSE 0 TRUE 0.2 2 1 TRUE 80.40347 40.20173 40.20173 80.40347 0.0969599 0.1967516 NA 0.8997917 31.37261 43.86162 50.45594 0.2053548 NA NA 
kable(summary(getSampleSizeRates(getDesignGroupSequential(kMax = 1, sided = 2),
    groups = 1, thetaH0 = 0.2, pi1 = c(0.4, 0.5)
)))
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
1 0.4 0.2 TRUE 1 TRUE 42.82849 0.0630022 0.3369978 0.025
1 0.5 0.2 TRUE 1 TRUE 19.28298 -0.0041704 0.4041704 0.025

Design plan summaries - survival

kable(summary(getSampleSizeSurvival(lambda2 = 0.3, hazardRatio = 1.2)))
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 NA NA NA NA NA NA NA NA NA
1 1.2 1 Schoenfeld TRUE 1.925409 2.310491 0.36 0.3 944.4775 1 12 81.60348 1 0 6 0 0 12 944.4775 979.2417 489.6209 489.6209 18 18 1.136042 
kable(summary(getSampleSizeSurvival(median1 = c(3.1, 3.2), median2 = 2.3)))
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 NA NA NA NA NA NA NA NA
1 3.1 1 Schoenfeld FALSE 2.3 0.2235959 0.3013683 0.7419355 352.3704 1 12 31.48792 1 6 0 0 12 352.3704 377.8551 188.9275 188.9275 18 18 0.8115388
1 3.2 1 Schoenfeld FALSE 2.3 0.2166085 0.3013683 0.7187500 287.8750 1 12 25.81196 1 6 0 0 12 287.8750 309.7436 154.8718 154.8718 18 18 0.7937124 
kable(summary(getSampleSizeSurvival(pi1 = 0.1, pi2 = 0.3)))
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 NA NA NA NA NA NA NA NA NA NA NA
1 0.1 1 Schoenfeld FALSE 0.3 78.94576 23.3203 0.00878 0.0297229 0.2953965 21.11295 1 12 12 8.888903 1 6 0 0 12 21.11295 106.6668 53.33342 53.33342 18 18 0.4260889 
piecewiseSurvivalTime <- list(
    "0 - <6" = 0.025,
    "6 - <9" = 0.04,
    "9 - <15" = 0.015,
    "15 - <21" = 0.01,
    ">= 21" = 0.007
)
kable(summary(getSampleSizeSurvival(
    piecewiseSurvivalTime = piecewiseSurvivalTime,
    hazardRatio = 1.2
)))
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 NA NA NA NA
1 1.2 1 Schoenfeld TRUE 944.4775 1 12 279.2448 1 6 0 0 12 944.4775 3350.938 1675.469 1675.469 18 18 1.136042

Simulation results summaries

Simulation results base

Simulation results base - means

design <- getDesignInverseNormal(
    kMax = 3, alpha = 0.025,
    futilityBounds = c(-0.5, 0), bindingFutility = FALSE,
    typeOfDesign = "WT", deltaWT = 0.25,
    informationRates = c(0.4, 0.7, 1)
)
kable(summary(getSimulationMeans(
    design = design, plannedSubjects = c(40, 70, 100),
    alternative = seq(0, 0.8, 0.2),
    stDev = 1.2,
    conditionalPower = 0.8,
    minNumberOfSubjectsPerStage = c(40, 20, 20),
    maxNumberOfSubjectsPerStage = c(40, 100, 100),
    thetaH1 = 0.6, stDevH1 = 1.5,
    maxNumberOfIterations = 1000,
    seed = 1234
)))
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 NA NA NA NA NA NA NA NA NA
1 0.0 1000 1234 1 0.8 1.2 40 40 40 0.6 1.5 FALSE 0 TRUE 2 TRUE 1000 0.027 0.004 0.537 0.320 0.548 151.4576 40.00000 NA
1 0.2 1000 1234 1 0.8 1.2 40 40 40 0.6 1.5 FALSE 0 TRUE 2 TRUE 1000 0.209 0.019 0.229 0.150 0.325 186.1055 40.00000 NA
1 0.4 1000 1234 1 0.8 1.2 40 40 40 0.6 1.5 FALSE 0 TRUE 2 TRUE 1000 0.626 0.039 0.072 0.061 0.397 179.3055 40.00000 NA
1 0.6 1000 1234 1 0.8 1.2 40 40 40 0.6 1.5 FALSE 0 TRUE 2 TRUE 1000 0.930 0.133 0.023 0.023 0.733 139.1249 40.00000 NA
1 0.8 1000 1234 1 0.8 1.2 40 40 40 0.6 1.5 FALSE 0 TRUE 2 TRUE 1000 0.993 0.283 0.003 0.003 0.935 108.2134 40.00000 NA
2 0.0 1000 1234 1 0.8 1.2 70 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 676 0.027 0.007 0.537 0.217 0.548 151.4576 99.29306 0.2205885
2 0.2 1000 1234 1 0.8 1.2 70 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 831 0.209 0.077 0.229 0.079 0.325 186.1055 98.63235 0.3111874
2 0.4 1000 1234 1 0.8 1.2 70 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 900 0.626 0.286 0.072 0.011 0.397 179.3055 96.77470 0.4008511
2 0.6 1000 1234 1 0.8 1.2 70 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 844 0.930 0.577 0.023 0.000 0.733 139.1249 93.50955 0.5292343
2 0.8 1000 1234 1 0.8 1.2 70 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 714 0.993 0.649 0.003 0.000 0.935 108.2134 88.86565 0.6065518
3 0.0 1000 1234 1 0.8 1.2 100 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 452 0.027 0.016 0.537 NA 0.548 151.4576 98.08735 0.3011155
3 0.2 1000 1234 1 0.8 1.2 100 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 675 0.209 0.113 0.229 NA 0.325 186.1055 95.02528 0.4507159
3 0.4 1000 1234 1 0.8 1.2 100 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 603 0.626 0.301 0.072 NA 0.397 179.3055 86.58091 0.5970424
3 0.6 1000 1234 1 0.8 1.2 100 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 267 0.930 0.220 0.023 NA 0.733 139.1249 75.66615 0.7150648
3 0.8 1000 1234 1 0.8 1.2 100 20 100 0.6 1.5 FALSE 0 TRUE 2 TRUE 65 0.993 0.061 0.003 NA 0.935 108.2134 73.28201 0.7396305

Simulation results base - rates

design <- getDesignFisher(
    kMax = 3, alpha = 0.025,
    alpha0Vec = c(0.5, 0.4), bindingFutility = FALSE,
    informationRates = c(0.4, 0.7, 1)
)
kable(summary(getSimulationRates(
    design = design, plannedSubjects = c(40, 70, 100),
    groups = 1,
    thetaH0 = 0.2,
    pi1 = seq(0.05, 0.2, 0.05),
    directionUpper = FALSE,
    maxNumberOfIterations = 1000,
    seed = 1234
)))
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 NA NA NA
1 0.05 1000 1234 FALSE 40 FALSE 0.2 TRUE 1 -0.15 1000 0.978 0.679 0.003 0.001 0.907 52.39 40 NA
1 0.10 1000 1234 FALSE 40 FALSE 0.2 TRUE 1 -0.10 1000 0.554 0.215 0.101 0.041 0.477 78.01 40 NA
1 0.15 1000 1234 FALSE 40 FALSE 0.2 TRUE 1 -0.05 1000 0.107 0.039 0.440 0.225 0.507 76.87 40 NA
1 0.20 1000 1234 FALSE 40 FALSE 0.2 TRUE 1 0.00 1000 0.011 0.009 0.812 0.568 0.822 58.03 40 NA
2 0.05 1000 1234 FALSE 70 FALSE 0.2 TRUE 1 -0.15 320 0.978 0.225 0.003 0.002 0.907 52.39 30 0.4488517
2 0.10 1000 1234 FALSE 70 FALSE 0.2 TRUE 1 -0.10 744 0.554 0.161 0.101 0.060 0.477 78.01 30 0.2752359
2 0.15 1000 1234 FALSE 70 FALSE 0.2 TRUE 1 -0.05 736 0.107 0.028 0.440 0.215 0.507 76.87 30 0.1404741
2 0.20 1000 1234 FALSE 70 FALSE 0.2 TRUE 1 0.00 423 0.011 0.001 0.812 0.244 0.822 58.03 30 0.0820006
3 0.05 1000 1234 FALSE 100 FALSE 0.2 TRUE 1 -0.15 93 0.978 0.074 0.003 NA 0.907 52.39 30 0.5698176
3 0.10 1000 1234 FALSE 100 FALSE 0.2 TRUE 1 -0.10 523 0.554 0.178 0.101 NA 0.477 78.01 30 0.3434445
3 0.15 1000 1234 FALSE 100 FALSE 0.2 TRUE 1 -0.05 493 0.107 0.040 0.440 NA 0.507 76.87 30 0.1870779
3 0.20 1000 1234 FALSE 100 FALSE 0.2 TRUE 1 0.00 178 0.011 0.001 0.812 NA 0.822 58.03 30 0.0932149

Simulation results base - survival

design <- getDesignInverseNormal(
    alpha = 0.05, kMax = 4, futilityBounds = c(0, 0, 0),
    sided = 1, typeOfDesign = "WT", deltaWT = 0.1
)
kable(summary(getSimulationSurvival(
    design = design,
    plannedEvents = c(40, 70, 100, 150),
    maxNumberOfSubjects = 600,
    thetaH0 = 1.2,
    pi1 = seq(0.1, 0.25, 0.05),
    pi2 = 0.2,
    allocation1 = 2,
    directionUpper = FALSE,
    conditionalPower = 0.8,
    minNumberOfEventsPerStage = c(40, 20, 20, 20),
    maxNumberOfEventsPerStage = c(40, 100, 100, 100),
    thetaH1 = 1,
    maxNumberOfIterations = 1000,
    seed = 1234
)))
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 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1 0.10 1000 1234 2 0.8 FALSE 40 40 40 0.8333333 0.2 78.94576 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0087800 0.0185953 0.4721647 11.834694 21.01490 0 576.898 384.5987 192.2993 40.000 1000 0.998 0.449 0.002 0.002 1.000 589.5810 95.000 NA
1 0.15 1000 1234 2 0.8 FALSE 70 20 100 0.8333333 0.2 51.18029 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0135432 0.0185953 0.7283156 10.474485 26.75633 0 522.454 348.3027 174.1513 40.000 1000 0.935 0.072 0.062 0.061 0.968 589.6864 156.300 NA
1 0.20 1000 1234 2 0.8 FALSE 100 20 100 0.8333333 0.2 37.27540 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0185953 0.0185953 1.0000000 9.500745 32.14197 0 474.542 316.3613 158.1807 39.999 1000 0.391 0.011 0.332 0.272 0.584 564.4954 210.599 NA
1 0.25 1000 1234 2 0.8 FALSE 150 20 100 0.8333333 0.2 28.91305 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0239735 0.0185953 1.2892242 8.704382 18.47824 0 434.728 289.8187 144.9093 40.000 1000 0.022 0.001 0.810 0.553 0.821 508.4393 129.300 NA
2 0.10 1000 1234 2 0.8 FALSE 40 40 40 0.8333333 0.2 78.94576 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0087800 0.0185953 0.4721647 28.461741 21.01490 0 600.000 400.0000 200.0000 140.000 549 0.998 0.548 0.002 0.000 1.000 589.5810 95.000 0.4404760
2 0.15 1000 1234 2 0.8 FALSE 70 20 100 0.8333333 0.2 51.18029 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0135432 0.0185953 0.7283156 23.558337 26.75633 0 600.000 400.0000 200.0000 140.000 867 0.935 0.602 0.062 0.001 0.968 589.6864 156.300 0.2555653
2 0.20 1000 1234 2 0.8 FALSE 100 20 100 0.8333333 0.2 37.27540 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0185953 0.0185953 1.0000000 20.359900 32.14197 0 600.000 400.0000 200.0000 139.999 717 0.391 0.092 0.332 0.052 0.584 564.4954 210.599 0.1304847
2 0.25 1000 1234 2 0.8 FALSE 150 20 100 0.8333333 0.2 28.91305 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0239735 0.0185953 1.2892242 18.048604 18.47824 0 600.000 400.0000 200.0000 140.000 446 0.022 0.002 0.810 0.176 0.821 508.4393 129.300 0.0793913
3 0.10 1000 1234 2 0.8 FALSE 40 40 40 0.8333333 0.2 78.94576 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0087800 0.0185953 0.4721647 52.123354 21.01490 0 600.000 400.0000 200.0000 240.000 1 0.998 0.001 0.002 0.000 1.000 589.5810 95.000 0.6021942
3 0.15 1000 1234 2 0.8 FALSE 70 20 100 0.8333333 0.2 51.18029 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0135432 0.0185953 0.7283156 39.687090 26.75633 0 600.000 400.0000 200.0000 240.000 264 0.935 0.232 0.062 0.000 0.968 589.6864 156.300 0.4813344
3 0.20 1000 1234 2 0.8 FALSE 100 20 100 0.8333333 0.2 37.27540 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0185953 0.0185953 1.0000000 33.483506 32.14197 0 600.000 400.0000 200.0000 239.999 573 0.391 0.149 0.332 0.008 0.584 564.4954 210.599 0.2567235
3 0.25 1000 1234 2 0.8 FALSE 150 20 100 0.8333333 0.2 28.91305 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0239735 0.0185953 1.2892242 29.106849 18.47824 0 600.000 400.0000 200.0000 240.000 268 0.022 0.008 0.810 0.081 0.821 508.4393 129.300 0.1205807
4 0.10 1000 1234 2 0.8 FALSE 40 40 40 0.8333333 0.2 78.94576 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0087800 0.0185953 0.4721647 NA 21.01490 0 NA NA NA 240.000 0 0.998 0.000 0.002 NA 1.000 589.5810 95.000 NA
4 0.15 1000 1234 2 0.8 FALSE 70 20 100 0.8333333 0.2 51.18029 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0135432 0.0185953 0.7283156 61.523800 26.75633 0 600.000 400.0000 200.0000 340.000 32 0.935 0.029 0.062 NA 0.968 589.6864 156.300 0.5405992
4 0.20 1000 1234 2 0.8 FALSE 100 20 100 0.8333333 0.2 37.27540 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0185953 0.0185953 1.0000000 51.129090 32.14197 0 600.000 400.0000 200.0000 339.999 416 0.391 0.139 0.332 NA 0.584 564.4954 210.599 0.3412708
4 0.25 1000 1234 2 0.8 FALSE 150 20 100 0.8333333 0.2 28.91305 37.2754 600 12 50 0 0 12 12 1.2 2 1 1 0.0239735 0.0185953 1.2892242 43.841187 18.47824 0 600.000 400.0000 200.0000 340.000 179 0.022 0.011 0.810 NA 0.821 508.4393 129.300 0.1487154

Simulation results multi-arm

Simulation results multi-arm - means

options("rpact.summary.output.size" = "medium") # small, medium, large
design <- getDesignFisher(alpha = 0.05, kMax = 3)
kable(summary(getSimulationMultiArmMeans(
    design = design,
    plannedSubjects = c(40, 70, 100),
    activeArms = 3,
    typeOfShape = "sigmoidEmax",
    gED50 = 2,
    typeOfSelection = "rBest",
    rValue = 2,
    stDev = 1.2,
    maxNumberOfIterations = 100,
    seed = 1234
)))
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 NA NA NA NA NA NA NA NA NA NA NA
1 100 1234 1 1.2 40 3 0.00 sigmoidEmax 0.0 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.05 0 0 0.00 0.00 3 338.2 NA
1 100 1234 1 1.2 40 3 0.12 sigmoidEmax 0.2 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.14 0 0 0.00 0.00 3 338.2 NA
1 100 1234 1 1.2 40 3 0.24 sigmoidEmax 0.4 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.29 0 0 0.02 0.02 3 332.8 NA
1 100 1234 1 1.2 40 3 0.36 sigmoidEmax 0.6 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.48 0 0 0.04 0.04 3 318.4 NA
1 100 1234 1 1.2 40 3 0.48 sigmoidEmax 0.8 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.71 0 0 0.07 0.07 3 305.8 NA
1 100 1234 1 1.2 40 3 0.60 sigmoidEmax 1.0 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.91 0 0 0.20 0.20 3 268.0 NA
2 100 1234 1 1.2 70 3 0.00 sigmoidEmax 0.0 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.05 0 0 0.02 0.02 2 338.2 0.0323812
2 100 1234 1 1.2 70 3 0.12 sigmoidEmax 0.2 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.14 0 0 0.02 0.02 2 338.2 0.0787109
2 100 1234 1 1.2 70 3 0.24 sigmoidEmax 0.4 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 98 0.29 0 0 0.04 0.04 2 332.8 0.1359605
2 100 1234 1 1.2 70 3 0.36 sigmoidEmax 0.6 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 96 0.48 0 0 0.16 0.16 2 318.4 0.2445856
2 100 1234 1 1.2 70 3 0.48 sigmoidEmax 0.8 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 93 0.71 0 0 0.24 0.24 2 305.8 0.3514472
2 100 1234 1 1.2 70 3 0.60 sigmoidEmax 1.0 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 80 0.91 0 0 0.40 0.40 2 268.0 0.4880927
3 100 1234 1 1.2 100 3 0.00 sigmoidEmax 0.0 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 98 0.05 0 NA NA 0.00 2 338.2 0.0257204
3 100 1234 1 1.2 100 3 0.12 sigmoidEmax 0.2 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 98 0.14 0 NA NA 0.01 2 338.2 0.1019180
3 100 1234 1 1.2 100 3 0.24 sigmoidEmax 0.4 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 94 0.29 0 NA NA 0.04 2 332.8 0.1782436
3 100 1234 1 1.2 100 3 0.36 sigmoidEmax 0.6 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 80 0.48 0 NA NA 0.05 2 318.4 0.2398366
3 100 1234 1 1.2 100 3 0.48 sigmoidEmax 0.8 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 69 0.71 0 NA NA 0.11 2 305.8 0.4298372
3 100 1234 1 1.2 100 3 0.60 sigmoidEmax 1.0 2 1 Dunnett rBest effectEstimate all NA 2 -Inf 40 0.91 0 NA NA 0.09 2 268.0 0.4534845

Simulation results multi-arm - rates

options("rpact.summary.output.size" = "medium") # small, medium, large
kable(summary(getSimulationMultiArmRates(
    design = design,
    plannedSubjects = c(40, 70, 100),
    activeArms = 3,
    typeOfShape = "userDefined",
    effectMatrix = matrix(c(
        0.1, 0.2, 0.3,
        0.2, 0.3, 0.4,
        0.2, 0.4, 0.4
    ), nrow = 3),
    typeOfSelection = "rBest",
    rValue = 2,
    directionUpper = FALSE,
    allocationRatioPlanned = 2,
    piControl = 0.4,
    conditionalPower = 0.8,
    minNumberOfSubjectsPerStage = c(40, 20, 20),
    maxNumberOfSubjectsPerStage = c(40, 100, 100),
    piH1 = 0.6, piControlH1 = 0.4,
    maxNumberOfIterations = 100,
    seed = 1234
)))
Warning: Argument unknown in getSimulationMultiArmRates(...): 'piH1' = 0.6 will
be ignored
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 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1 100 1234 2 0.8 FALSE 40 40 40 3 1 userDefined 0.2 0.4 NA 0.4 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 1.00 0 0 0.18 0.18 3 259.87 NA
1 100 1234 2 0.8 FALSE 70 20 100 3 2 userDefined 0.4 0.4 NA 0.4 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.94 0 0 0.00 0.00 3 484.65 NA
1 100 1234 2 0.8 FALSE 100 20 100 3 3 userDefined 0.4 0.4 NA 0.4 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.40 0 0 0.01 0.01 3 622.67 NA
2 100 1234 2 0.8 FALSE 40 40 40 3 1 userDefined 0.2 0.4 NA 0.4 1 Dunnett rBest effectEstimate all NA 2 -Inf 82 1.00 0 0 0.48 0.48 2 259.87 0.7129385
2 100 1234 2 0.8 FALSE 70 20 100 3 2 userDefined 0.4 0.4 NA 0.4 1 Dunnett rBest effectEstimate all NA 2 -Inf 100 0.94 0 0 0.13 0.13 2 484.65 0.1913190
2 100 1234 2 0.8 FALSE 100 20 100 3 3 userDefined 0.4 0.4 NA 0.4 1 Dunnett rBest effectEstimate all NA 2 -Inf 99 0.40 0 0 0.02 0.02 2 622.67 0.0451833
3 100 1234 2 0.8 FALSE 40 40 40 3 1 userDefined 0.2 0.4 NA 0.4 1 Dunnett rBest effectEstimate all NA 2 -Inf 34 1.00 0 NA NA 0.11 2 259.87 0.9511866
3 100 1234 2 0.8 FALSE 70 20 100 3 2 userDefined 0.4 0.4 NA 0.4 1 Dunnett rBest effectEstimate all NA 2 -Inf 87 0.94 0 NA NA 0.11 2 484.65 0.7013294
3 100 1234 2 0.8 FALSE 100 20 100 3 3 userDefined 0.4 0.4 NA 0.4 1 Dunnett rBest effectEstimate all NA 2 -Inf 97 0.40 0 NA NA 0.02 2 622.67 0.1104366

Simulation results multi-arm - survival

options("rpact.summary.output.size" = "medium") # small, medium, large
kable(summary(getSimulationMultiArmSurvival(
    seed = 1234,
    getDesignInverseNormal(informationRates = c(0.2, 0.6, 1)),
    typeOfShape = "linear", activeArms = 4,
    plannedEvents = c(10, 30, 50), omegaMaxVector = seq(0.3, 0.6, 0.1),
    adaptations = rep(TRUE, 2), directionUpper = FALSE,
    minNumberOfEventsPerStage = c(10, 4, 4), maxNumberOfEventsPerStage = c(10, 100, 100),
    maxNumberOfIterations = 10,
    calcEventsFunction = function(..., stage, minNumberOfEventsPerStage) {
        return(ifelse(stage == 3, 33, minNumberOfEventsPerStage[stage]))
    }
)))
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 NA NA NA NA NA NA NA NA NA NA NA NA
1 10 1234 1 FALSE 10 10 10 4 0.3 linear 0.3 1 Dunnett best effectEstimate all NA NA -Inf 10 0.3 0 0 0.0 0.0 4 43.7 NA
1 10 1234 1 FALSE 10 10 10 4 0.4 linear 0.4 1 Dunnett best effectEstimate all NA NA -Inf 10 0.4 0 0 0.0 0.0 4 47.0 NA
1 10 1234 1 FALSE 10 10 10 4 0.5 linear 0.5 1 Dunnett best effectEstimate all NA NA -Inf 10 0.7 0 0 0.0 0.0 4 40.4 NA
1 10 1234 1 FALSE 10 10 10 4 0.6 linear 0.6 1 Dunnett best effectEstimate all NA NA -Inf 10 0.3 0 0 0.0 0.0 4 43.7 NA
2 10 1234 1 FALSE 30 4 100 4 0.3 linear 0.3 1 Dunnett best effectEstimate all NA NA -Inf 10 0.3 0 0 0.1 0.1 1 43.7 0.1322722
2 10 1234 1 FALSE 30 4 100 4 0.4 linear 0.4 1 Dunnett best effectEstimate all NA NA -Inf 10 0.4 0 0 0.0 0.0 1 47.0 0.3350095
2 10 1234 1 FALSE 30 4 100 4 0.5 linear 0.5 1 Dunnett best effectEstimate all NA NA -Inf 10 0.7 0 0 0.2 0.2 1 40.4 0.3247879
2 10 1234 1 FALSE 30 4 100 4 0.6 linear 0.6 1 Dunnett best effectEstimate all NA NA -Inf 10 0.3 0 0 0.1 0.1 1 43.7 0.1917470
3 10 1234 1 FALSE 50 4 100 4 0.3 linear 0.3 1 Dunnett best effectEstimate all NA NA -Inf 9 0.3 0 NA NA 0.2 1 43.7 0.2868250
3 10 1234 1 FALSE 50 4 100 4 0.4 linear 0.4 1 Dunnett best effectEstimate all NA NA -Inf 10 0.4 0 NA NA 0.4 1 47.0 0.6076832
3 10 1234 1 FALSE 50 4 100 4 0.5 linear 0.5 1 Dunnett best effectEstimate all NA NA -Inf 8 0.7 0 NA NA 0.5 1 40.4 0.6093950
3 10 1234 1 FALSE 50 4 100 4 0.6 linear 0.6 1 Dunnett best effectEstimate all NA NA -Inf 9 0.3 0 NA NA 0.2 1 43.7 0.3747728

Simulation results enrichment

Simulation results enrichment - means

options("rpact.summary.output.size" = "medium") # small, medium, large

design <- getDesignFisher(alpha = 0.05, kMax = 3)

subGroups <- c("S", "R")
prevalences <- c(0.1, 0.9)
alternative <- c(0.4, 0.5)
effectList <- list(
    subGroups = subGroups, prevalences = prevalences,
    stDevs = 1,
    effects = matrix(alternative, byrow = TRUE, ncol = 2)
)

kable(summary(getSimulationEnrichmentMeans(
    design = design,
    plannedSubjects = c(40, 70, 100),
    effectList = effectList,
    typeOfSelection = "rBest",
    rValue = 2,
    maxNumberOfIterations = 100,
    seed = 1234
)))
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 NA NA NA NA NA NA NA NA NA
1 100 1234 1 40 2 0.4 0.5 1 Simes TRUE rBest effectEstimate all NA 2 -Inf 100 0.56 0 0 0.03 0.03 2 97 NA
2 100 1234 1 70 2 0.4 0.5 1 Simes TRUE rBest effectEstimate all NA 2 -Inf 97 0.56 0 0 0.04 0.04 2 97 0.3091665
3 100 1234 1 100 2 0.4 0.5 1 Simes TRUE rBest effectEstimate all NA 2 -Inf 93 0.56 0 NA NA 0.04 2 97 0.3745484

Simulation results enrichment - rates

options("rpact.summary.output.size" = "large") # small, medium, large

design <- getDesignFisher(alpha = 0.05, kMax = 3)

subGroups <- c("S", "R")
prevalences <- c(0.1, 0.9)
pi2 <- c(0.3, 0.4)
piTreatments <- c(0.4, 0.5)
effectList <- list(
    subGroups = subGroups, prevalences = prevalences,
    piControl = pi2, piTreatments = matrix(piTreatments, byrow = TRUE, ncol = 2)
)

kable(summary(getSimulationEnrichmentRates(
    design = design,
    plannedSubjects = c(40, 70, 100),
    effectList = effectList,
    typeOfSelection = "rBest",
    rValue = 2,
    maxNumberOfIterations = 100,
    seed = 1234
)))
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 NA NA NA NA NA NA NA NA NA NA NA NA
1 100 1234 1 TRUE 40 2 0.4 0.5 1 Simes TRUE NA NA rBest effectEstimate all NA 2 -Inf 100 0.18 0 0 0.02 0.02 2 97.9 NA
2 100 1234 1 TRUE 70 2 0.4 0.5 1 Simes TRUE NA NA rBest effectEstimate all NA 2 -Inf 98 0.18 0 0 0.03 0.03 2 97.9 0.0689093
3 100 1234 1 TRUE 100 2 0.4 0.5 1 Simes TRUE NA NA rBest effectEstimate all NA 2 -Inf 95 0.18 0 NA NA 0.04 2 97.9 0.0731194

Simulation results enrichment - survival

options("rpact.summary.output.size" = "medium") # small, medium, large

design <- getDesignFisher(alpha = 0.05, kMax = 3)

subGroups <- c("S1", "S2", "S12", "R")
prevalences <- c(0.1, 0.3, 0.4, 0.2)
hazardRatios <- c(0.4, 0.5, 0.6, 0.7, 0.6, 0.6, 0.6, 0.8)
effectList <- list(
    subGroups = subGroups, prevalences = prevalences,
    hazardRatios = matrix(hazardRatios, byrow = TRUE, ncol = 4)
)

kable(summary(getSimulationEnrichmentSurvival(
    design = design,
    plannedEvents = c(40, 70, 100),
    effectList = effectList,
    conditionalPower = 0.8,
    minNumberOfEventsPerStage = c(40, 20, 20),
    maxNumberOfEventsPerStage = c(40, 100, 100),
    maxNumberOfIterations = 100,
    seed = 1234
)))
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 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
1 100 1234 1 0.8 TRUE 40 40 40 3 0.4 0.5 0.6 0.7 1 Simes TRUE best effectEstimate all NA NA -Inf 100 0 0 0 0 0 3 240.000 NA
1 100 1234 1 0.8 TRUE 40 40 40 3 0.6 0.6 0.6 0.8 2 Simes TRUE best effectEstimate all NA NA -Inf 100 0 0 0 0 0 3 238.606 NA
2 100 1234 1 0.8 TRUE 70 20 100 3 0.4 0.5 0.6 0.7 1 Simes TRUE best effectEstimate all NA NA -Inf 100 0 0 0 0 0 1 240.000 0.0006467
2 100 1234 1 0.8 TRUE 70 20 100 3 0.6 0.6 0.6 0.8 2 Simes TRUE best effectEstimate all NA NA -Inf 100 0 0 0 0 0 1 238.606 0.0114462
3 100 1234 1 0.8 TRUE 100 20 100 3 0.4 0.5 0.6 0.7 1 Simes TRUE best effectEstimate all NA NA -Inf 100 0 0 NA NA 0 1 240.000 0.0000007
3 100 1234 1 0.8 TRUE 100 20 100 3 0.6 0.6 0.6 0.8 2 Simes TRUE best effectEstimate all NA NA -Inf 100 0 0 NA NA 0 1 238.606 0.0000637

Analysis results summaries

Create three different designs

design1 <- getDesignInverseNormal(
    kMax = 4, alpha = 0.02,
    futilityBounds = c(-0.5, 0, 0.5), bindingFutility = FALSE,
    typeOfDesign = "asKD", gammaA = 1.2,
    informationRates = c(0.15, 0.4, 0.7, 1)
)

design3 <- getDesignConditionalDunnett(
    alpha = 0.02,
    informationAtInterim = 0.4, secondStageConditioning = TRUE
)

Analysis results base

Analysis results base - means

simpleDataExampleMeans1 <- getDataset(
    n = c(120, 130, 130),
    means = c(0.45, 0.51, 0.45) * 100,
    stDevs = c(1.3, 1.4, 1.2) * 100
)

kable(summary(getAnalysisResults(
    design = design1, dataInput = simpleDataExampleMeans1,
    nPlanned = 130, thetaH0 = 30, thetaH1 = 60, assumedStDev = 100
)))
Calculation of final confidence interval performed for kMax = 4 (for kMax > 2, it is theoretically shown that it is valid only if no sample size change was performed)
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 100 FALSE TRUE continue 0.0433141 NA 10.26860 79.73140 0.4999990 NA NA NA NA
NA 100 FALSE TRUE continue 0.1706364 NA 25.95222 70.71865 0.0661964 NA NA NA NA
NA 100 FALSE TRUE reject and stop 0.3851373 NA 30.89970 63.00417 0.0140858 0.0108264 31.78436 60.14634 46.18327
130 100 FALSE TRUE NA NA 0.9991215 NA NA NA NA NA NA NA 
simpleDataExampleMeans2 <- getDataset(
    n1 = c(23, 13, 22, 13),
    n2 = c(22, 11, 22, 11),
    means1 = c(2.7, 2.5, 4.5, 2.5) * 100,
    means2 = c(1, 1.1, 1.3, 1) * 100,
    stds1 = c(1.3, 2.4, 2.2, 1.3) * 100,
    stds2 = c(1.2, 2.2, 2.1, 1.3) * 100
)

kable(summary(getAnalysisResults(
    design = design1, dataInput = simpleDataExampleMeans2,
    equalVariances = TRUE, directionUpper = TRUE
)))
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 187.2243 reject and stop 0.5468917 56.78008 283.2199 0.0002099 0.0000216 93.31004 246.69 170
FALSE TRUE TRUE 187.2243 reject and stop 0.7525340 50.70848 267.8984 0.0005875 NA NA NA NA
FALSE TRUE TRUE 187.2243 reject and stop 0.9999947 129.27061 304.6062 0.0000020 NA NA NA NA
FALSE TRUE TRUE 187.2243 reject NA 127.41390 266.0163 0.0000020 NA NA NA NA

Analysis results base - rates

simpleDataExampleRates1 <- getDataset(
    n = c(8, 10, 9, 11),
    events = c(4, 5, 5, 6)
)

kable(summary(getAnalysisResults(
    design = design1, dataInput = simpleDataExampleRates1,
    stage = 3, thetaH0 = 0.75, normalApproximation = TRUE, directionUpper = FALSE,
    nPlanned = 10
)))
Calculation of final confidence interval performed for kMax = 4 (for kMax > 2, it is theoretically shown that it is valid only if no sample size change was performed)
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 NA TRUE continue 0.0668905 NA 0.1438806 0.8561194 0.4991827 NA NA NA NA
FALSE NA TRUE continue 0.2721816 NA 0.2402928 0.7597072 0.0285358 NA NA NA NA
FALSE NA TRUE reject and stop 0.5084429 NA 0.3074465 0.7273553 0.0079346 0.0087165 0.2996945 0.7142643 0.5023876
FALSE 10 TRUE NA NA 0.9310112 NA NA NA NA NA NA NA 
simpleDataExampleRates2 <- getDataset(
    n1 = c(17, 23, 22),
    n2 = c(18, 20, 19),
    events1 = c(11, 12, 17),
    events2 = c(5, 10, 7)
)

kable(summary(getAnalysisResults(design1, simpleDataExampleRates2,
    thetaH0 = 0, stage = 2, directionUpper = TRUE,
    normalApproximation = FALSE, pi1 = 0.9, pi2 = 0.3, nPlanned = c(20, 20)
)))
Repeated confidence intervals will be calculated under the normal approximation
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
FALSE NA TRUE continue 0.0863239 NA -0.1113128 0.7107522 0.3048243
FALSE NA TRUE continue 0.0212523 NA -0.1246246 0.4290468 0.4999990
FALSE 20 TRUE NA NA 0.7256777 NA NA NA
FALSE 20 TRUE NA NA 0.9884129 NA NA NA

Analysis results base - survival

simpleDataExampleSurvival <- getDataset(
    overallEvents = c(8, 15, 29),
    overallAllocationRatios = c(1, 1, 1),
    overallLogRanks = c(1.52, 1.38, 2.9)
)

kable(simpleDataExampleSurvival$getNumberOfGroups())
x
2 
kable(summary(getAnalysisResults(design1, simpleDataExampleSurvival, directionUpper = TRUE)))
Calculation of final confidence interval performed for kMax = 4 (for kMax > 2, it is theoretically shown that it is valid only if no sample size change was performed)
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
TRUE TRUE continue 0.0588148 0.3849854 22.290362 0.4999990 NA NA NA NA
TRUE TRUE continue 0.0350165 0.4989526 7.357912 0.3739998 NA NA NA NA
TRUE TRUE reject and stop 0.5133864 1.1383956 6.825256 0.0077496 0.0086572 1.160127 5.89997 2.686325
TRUE TRUE NA NA NA NA NA NA NA NA NA

Analysis results multi-arm

Analysis results multi-arm - means

dataExampleMeans <- getDataset(
    n1 = c(13, 25),
    n2 = c(15, NA),
    n3 = c(14, 27),
    n4 = c(12, 29),
    means1 = c(242, 222),
    means2 = c(188, NA),
    means3 = c(267, 277),
    means4 = c(92, 122),
    stDevs1 = c(244, 221),
    stDevs2 = c(212, NA),
    stDevs3 = c(256, 232),
    stDevs4 = c(215, 227)
)

kable(summary(getAnalysisResults(
    design = design3, dataInput = dataExampleMeans, stage = 2, thetaH0 = 120,
    directionUpper = TRUE, normalApproximation = TRUE,
    assumedStDevs = c(24, 25, 23)
)))
Warning: 'assumedStDevs' (24, 25, 23) will be ignored because 'nPlanned' is not
defined
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
1 TRUE 24 TRUE Dunnett overallPooled NA NA NA NA NA
1 TRUE 25 TRUE Dunnett overallPooled NA NA NA NA NA
1 TRUE 23 TRUE Dunnett overallPooled NA NA NA NA NA
2 TRUE 24 TRUE Dunnett overallPooled 0.0040668 NA -7.24414 225.3873 0.5000000
2 TRUE 25 TRUE Dunnett overallPooled 0.0020592 NA NA NA NA
2 TRUE 23 TRUE Dunnett overallPooled 0.0061624 NA 39.38916 267.5353 0.3767884

Analysis results multi-arm - rates

dataExampleRates <- getDataset(
    n1 = c(23, 25),
    n2 = c(25, NA),
    n3 = c(24, 27),
    n4 = c(22, 29),
    events1 = c(15, 12),
    events2 = c(19, NA),
    events3 = c(18, 22),
    events4 = c(12, 13)
)

kable(summary(getAnalysisResults(
    design = design1, dataInput = dataExampleRates,
    intersectionTest = "Bonferroni", nPlanned = c(20, 20),
    directionUpper = TRUE, piTreatments = c(0.4, 0.6, 0.5)
)))
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
1 NA 0.4 Bonferroni TRUE TRUE 0.4901961 0.0227127 NA -0.3361707 0.5131640 0.5000000
1 NA 0.6 Bonferroni TRUE TRUE 0.4901961 0.0282828 NA -0.2215673 0.5884070 0.5000000
1 NA 0.5 Bonferroni TRUE TRUE 0.4901961 0.0282828 NA -0.2371424 0.5828731 0.5000000
2 NA 0.4 Bonferroni TRUE TRUE 0.4901961 0.0087985 NA -0.2360829 0.3555550 0.5000000
2 NA 0.6 Bonferroni TRUE TRUE 0.4901961 NA NA NA NA NA
2 NA 0.5 Bonferroni TRUE TRUE 0.4901961 0.3345541 NA -0.0031828 0.5489880 0.0178595
3 20 0.4 Bonferroni TRUE TRUE 0.4901961 NA 0.0004228 NA NA NA
3 20 0.6 Bonferroni TRUE TRUE 0.4901961 NA NA NA NA NA
3 20 0.5 Bonferroni TRUE TRUE 0.4901961 NA 0.2769464 NA NA NA
4 20 0.4 Bonferroni TRUE TRUE 0.4901961 NA 0.0016414 NA NA NA
4 20 0.6 Bonferroni TRUE TRUE 0.4901961 NA NA NA NA NA
4 20 0.5 Bonferroni TRUE TRUE 0.4901961 NA 0.3543617 NA NA NA

Analysis results multi-arm - survival

dataExampleSurvival <- getDataset(
    events1 = c(25, 32),
    events2 = c(18, NA),
    events3 = c(22, 36),
    logRanks1 = c(2.2, 1.8),
    logRanks2 = c(1.99, NA),
    logRanks3 = c(2.32, 2.11)
)

kable(summary(getAnalysisResults(
    design = design3, dataInput = dataExampleSurvival,
    intersectionTest = "Dunnett", directionUpper = TRUE, thetaH0 = 2
)))
Warning in is.na(parameterValues): is.na() auf Nicht-(Liste oder Vektor) des
Typs 'environment' angewendet
object NA NA NA NA NA NA NA
1 TRUE Dunnett NA NA NA NA NA
1 TRUE Dunnett NA NA NA NA NA
1 TRUE Dunnett NA NA NA NA NA
2 TRUE Dunnett 0.0097615 NA 1.121635 3.910922 0.491312
2 TRUE Dunnett 0.0097615 NA NA NA NA
2 TRUE Dunnett 0.0102570 NA 1.217372 4.196898 0.491312

Analysis results enrichment

Analysis results enrichment - means

dataS1 <- getDataset(
    means1 = c(13.2, 12.8),
    means2 = c(11.1, 10.8),
    stDev1 = c(3.4, 3.3),
    stDev2 = c(2.9, 3.5),
    n1 = c(21, 22),
    n2 = c(19, 21)
)
dataNotS1 <- getDataset(
    means1 = c(11.8, NA),
    means2 = c(11.5, NA),
    stDev1 = c(3.6, NA),
    stDev2 = c(2.7, NA),
    n1 = c(15, NA),
    n2 = c(13, NA)
)
dataExampleMeans <- getDataset(S1 = dataS1, R = dataNotS1)

kable(summary(getAnalysisResults(
    design = design1, dataInput = dataExampleMeans, varianceOption = "pooledFromFull",
    intersectionTest = "SpiessensDebois", nPlanned = c(20, 20), directionUpper = TRUE, assumedStDevs = 5
)))
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
1 S1 NA 5 SpiessensDebois pooledFromFull FALSE TRUE TRUE 0.0835064 NA -1.1012667 5.301266 0.3255018
1 F NA 5 SpiessensDebois pooledFromFull FALSE TRUE TRUE 0.0742736 NA -1.0966271 3.816541 0.4097149
2 S1 NA 5 SpiessensDebois pooledFromFull FALSE TRUE TRUE 0.3317830 NA 0.0251025 4.066298 0.0182276
2 F NA 5 SpiessensDebois pooledFromFull FALSE TRUE TRUE NA NA NA NA NA
3 S1 20 5 SpiessensDebois pooledFromFull FALSE TRUE TRUE NA 0.6079628 NA NA NA
3 F 20 5 SpiessensDebois pooledFromFull FALSE TRUE TRUE NA NA NA NA NA
4 S1 20 5 SpiessensDebois pooledFromFull FALSE TRUE TRUE NA 0.7684142 NA NA NA
4 F 20 5 SpiessensDebois pooledFromFull FALSE TRUE TRUE NA NA NA NA NA

Analysis results enrichment - rates

S1 <- getDataset(
    events2 = c(16, 19),
    sampleSizes2 = c(33, 34),
    events1 = c(26, 29),
    sampleSizes1 = c(35, 32)
)
S2 <- getDataset(
    events2 = c(12, 15),
    sampleSizes2 = c(36, 31),
    events1 = c(22, 24),
    sampleSizes1 = c(31, 39)
)
F <- getDataset(
    events2 = c(65, 54),
    sampleSizes2 = c(83, 84),
    events1 = c(66, 59),
    sampleSizes1 = c(85, 82)
)

dataExampleRates <- getDataSet(S1 = S1, S2 = S2, F = F)

kable(summary(getAnalysisResults(
    design = design1, dataInput = dataExampleRates, stratifiedAnalysis = FALSE,
    intersectionTest = "Simes", nPlanned = c(20, 20),
    piControls = c(0.6, 0.2, 0.3),
    directionUpper = TRUE
)))
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
1 S1 NA 0.6 FALSE TRUE TRUE Simes 0.8208955 0.0899568 NA -0.1179296 0.5727577 0.2792515
1 S2 NA 0.2 FALSE TRUE TRUE Simes 0.6571429 0.2063469 NA -0.0157611 0.6671208 0.0310478
1 F NA 0.3 FALSE TRUE TRUE Simes 0.7485030 0.0076311 NA -0.2136954 0.2019493 0.5000000
2 S1 NA 0.6 FALSE TRUE TRUE Simes 0.8208955 0.7054161 NA 0.0523286 0.5325845 0.0009566
2 S2 NA 0.2 FALSE TRUE TRUE Simes 0.6571429 0.3252399 NA -0.0457762 0.4905419 0.0191307
2 F NA 0.3 FALSE TRUE TRUE Simes 0.7485030 0.0123316 NA -0.1199849 0.1969206 0.5000000
3 S1 20 0.6 FALSE TRUE TRUE Simes 0.8208955 NA 0.9368132 NA NA NA
3 S2 20 0.2 FALSE TRUE TRUE Simes 0.6571429 NA 0.9478476 NA NA NA
3 F 20 0.3 FALSE TRUE TRUE Simes 0.7485030 NA 0.2416759 NA NA NA
4 S1 20 0.6 FALSE TRUE TRUE Simes 0.8208955 NA 0.9734788 NA NA NA
4 S2 20 0.2 FALSE TRUE TRUE Simes 0.6571429 NA 0.9947882 NA NA NA
4 F 20 0.3 FALSE TRUE TRUE Simes 0.7485030 NA 0.7339077 NA NA NA

Analysis results enrichment - survival

S <- getDataset(
    events = c(16, 19),
    logRanks = c(1.5, 1.3)
)

R <- getDataset(
    events = c(16, 29),
    logRanks = c(1.5, 1.3)
)
dataExampleSurvival <- getDataset(S1 = S, F = R)

kable(summary(getAnalysisResults(
    design = design1, dataInput = dataExampleSurvival,
    intersectionTest = "Simes", nPlanned = c(20, 20), directionUpper = TRUE
)))
Test statistics from full (and sub-populations) need to be stratified log-rank tests
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
1 S1 NA TRUE Simes TRUE 0.0573598 NA 0.4532802 9.887240 0.500000
1 F NA TRUE Simes TRUE 0.0573598 NA 0.4532802 9.887240 0.500000
2 S1 NA TRUE Simes TRUE 0.1311738 NA 0.7029320 5.334245 0.096959
2 F NA TRUE Simes TRUE 0.1311738 NA 0.7336099 4.381324 0.096959
3 S1 20 TRUE Simes TRUE NA 0.5341974 NA NA NA
3 F 20 TRUE Simes TRUE NA 0.4550737 NA NA NA
4 S1 20 TRUE Simes TRUE NA 0.8075553 NA NA NA
4 F 20 TRUE Simes TRUE NA 0.7247529 NA NA NA

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.