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rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

# Summary

This R Markdown document provides an example for updating the group sequential boundaries when using an $$\alpha$$-spending function approach based on observed information rates in rpact. Since version 3.1 of rpact, an additional option in the getAnalysisResults() function provides an easy way to perform an analysis with critical values that are calculated subsequently during the stages of the trial.

# 1 Introduction

Group-sequential designs based on $$\alpha$$-spending functions protect the Type I error exactly even if the pre-planned interim schedule is not exactly adhered to. However, this requires re-calculation of the group sequential boundaries at each interim analysis based on actually observed information fractions. Unless deviations from the planned information fractions are substantial, the re-calculated boundaries are quite similar to the pre-planned boundaries and the re-calculation will affect the actual test decision only on rare occasions.

Importantly, it is not allowed that the timing of future interim analyses is “motivated” by results from earlier interim analyses as this could inflate the Type I error rate. Deviations from the planned information fractions should thus only occur due to operational reasons (as it is difficult to hit the exact number of events exactly in a real trial) or due to external evidence.

The general principles for these boundary re-calculation are as follows (see also, Wassmer & Brannath, 2016, p78f):

• Updates at interim analyses prior to the final analysis:
• Information fractions are updated according to the actually observed information fraction at the interim analysis relative to the planned maximum information.
• The planned $$\alpha$$-spending function is then applied to these updated information fractions.
• Updates at the final analysis in case the observed information at the final analysis is larger (“over-running”) or smaller (“under-running”) than the planned maximum information:
• Information fractions are updated according to the actually observed information fraction at all interim analyses relative to the observed maximum information. $$\Rightarrow$$ Information fraction at final analysis is re-set to 1 but information fractions for earlier interim analyses are also changed.
• The originally planned $$\alpha$$-spending function cannot be applied to these updated information fractions because this would modify the critical boundaries of earlier interim analyses which is clearly not allowed. Instead, one uses the $$\alpha$$ that has actually been spent at earlier interim analyses and spends all remaining $$\alpha$$ at the final analysis.

This general principle be implemented via a user-defined $$\alpha$$-spending function and is illustrated for an example trial with a survival endpoint below. We provide two solutions to the problem: the first is a way how existing tools in rpact can directly be used to solve the problem, the second is an automatic recalculation of the boundaries using a new parameter set (maxInformation and informationEpsilon) which is available in the getAnalysisResults() function since rpact version 3.1. This solution is described in Section 7 at the end of this document.

library(rpact)
packageVersion("rpact") # version should be version 3.1 or later
## [1] '3.3.2'

# 2 Original trial design

The original trial design for this example is based on a standard O’Brien & Fleming type $$\alpha$$-spending function with planned efficacy interim analyses after 50% and 75% of information as specified below.

# Initial design
design <- getDesignGroupSequential(
sided = 1, alpha = 0.025, beta = 0.2,
informationRates = c(0.5, 0.75, 1), typeOfDesign = "asOF"
)

# Initial sample size calculation
sampleSizeResult <- getSampleSizeSurvival(
design = design,
lambda2 = log(2) / 60, hazardRatio = 0.75,
dropoutRate1 = 0.025, dropoutRate2 = 0.025, dropoutTime = 12,
accrualTime = 0, accrualIntensity = 30,
maxNumberOfSubjects = 1000
)

# Summarize design
kable(summary(sampleSizeResult))

Sample size calculation for a survival endpoint

Sequential analysis with a maximum of 3 looks (group sequential design), overall significance level 2.5% (one-sided). The sample size was calculated for a two-sample logrank test, H0: hazard ratio = 1, H1: hazard ratio = 0.75, control lambda(2) = 0.012, maximum number of subjects = 1000, accrual time = 33.333, accrual intensity = 30, dropout rate(1) = 0.025, dropout rate(2) = 0.025, dropout time = 12, power 80%.

Stage 1 2 3
Information rate 50% 75% 100%
Efficacy boundary (z-value scale) 2.963 2.359 2.014
Overall power 0.1680 0.5400 0.8000
Expected number of subjects 1000.0
Number of subjects 1000.0 1000.0 1000.0
Cumulative number of events 193.4 290.1 386.8
Analysis time 39.1 52.7 69.1
Expected study duration 58.0
Cumulative alpha spent 0.0015 0.0096 0.0250
One-sided local significance level 0.0015 0.0092 0.0220
Efficacy boundary (t) 0.653 0.758 0.815
Exit probability for efficacy (under H0) 0.0015 0.0081
Exit probability for efficacy (under H1) 0.1680 0.3720

Legend:

• (t): treatment effect scale

# 3 Boundary and power update at the first interim analysis

Assume that the first interim was conducted after 205 rather than the planned 194 events.

The updated design is calculated as per the code below. Note that for the calculation of boundary values on the treatment effect scale, we use the function getPowerSurvival() with the updated design rather than the function getSampleSizeSurvival() as we are only updating the boundary, not the sample size or the maximum number of events.

# Update design using observed information fraction at first interim.
# Information fraction of later interim analyses is not changed.
designUpdate1 <- getDesignGroupSequential(
sided = 1, alpha = 0.025, beta = 0.2,
informationRates = c(205 / 387, 0.75, 1), typeOfDesign = "asOF"
)

# Recalculate the power to get boundary values on the effect scale
# (Use original maxNumberOfEvents and sample size)
powerUpdate1 <- getPowerSurvival(
design = designUpdate1,
lambda2 = log(2) / 60, hazardRatio = 0.75,
dropoutRate1 = 0.025, dropoutRate2 = 0.025, dropoutTime = 12,
accrualTime = 0, accrualIntensity = 30,
maxNumberOfSubjects = 1000, maxNumberOfEvents = 387, directionUpper = FALSE
)

The updated information rates and corresponding boundaries as per the output above are summarized as follows:

Power calculation for a survival endpoint

Sequential analysis with a maximum of 3 looks (group sequential design), overall significance level 2.5% (one-sided). The results were calculated for a two-sample logrank test, H0: hazard ratio = 1, power directed towards smaller values, H1: hazard ratio = 0.75, control lambda(2) = 0.012, maximum number of subjects = 1000, maximum number of events = 387, accrual time = 33.333, accrual intensity = 30, dropout rate(1) = 0.025, dropout rate(2) = 0.025, dropout time = 12.

Stage 1 2 3
Information rate 53% 75% 100%
Efficacy boundary (z-value scale) 2.867 2.366 2.015
Overall power 0.2097 0.5391 0.8001
Expected number of subjects 1000.0
Number of subjects 1000.0 1000.0 1000.0
Expected number of events 317.0
Cumulative number of events 205.0 290.2 387.0
Analysis time 40.6 52.7 69.1
Expected study duration 57.8
Cumulative alpha spent 0.0021 0.0096 0.0250
One-sided local significance level 0.0021 0.0090 0.0220
Efficacy boundary (t) 0.670 0.758 0.815
Exit probability for efficacy (under H0) 0.0021 0.0076
Exit probability for efficacy (under H1) 0.2097 0.3294

Legend:

• (t): treatment effect scale

# 4 Boundary and power update at the second interim analysis

Assume that the efficacy boundary was not crossed at the first interim analysis and the trial continued to the second interim analysis which was conducted after 285 rather than the planned 291 events. The updated design is calculated in the same way as for the first interim analysis as per the code below. The idea is to use the cumulative $$\alpha$$ spent from the first stage and an updated cumulative $$\alpha$$ that is spent for the second stage. For the second stage, this can be obtained with the original O’Brien & Fleming $$\alpha$$-spending function:

# Update design using observed information fraction at first and second interim.
designUpdate2 <- getDesignGroupSequential(
sided = 1, alpha = 0.025, beta = 0.2,
informationRates = c(205 / 387, 285 / 387, 1), typeOfDesign = "asOF"
)

# Recalculate power to get boundary values on effect scale
# (Use original maxNumberOfEvents and sample size)
powerUpdate2 <- getPowerSurvival(
design = designUpdate2,
lambda2 = log(2) / 60, hazardRatio = 0.75,
dropoutRate1 = 0.025, dropoutRate2 = 0.025, dropoutTime = 12,
accrualTime = 0, accrualIntensity = 30,
maxNumberOfSubjects = 1000, maxNumberOfEvents = 387, directionUpper = FALSE
)
kable(summary(powerUpdate2))

Power calculation for a survival endpoint

Sequential analysis with a maximum of 3 looks (group sequential design), overall significance level 2.5% (one-sided). The results were calculated for a two-sample logrank test, H0: hazard ratio = 1, power directed towards smaller values, H1: hazard ratio = 0.75, control lambda(2) = 0.012, maximum number of subjects = 1000, maximum number of events = 387, accrual time = 33.333, accrual intensity = 30, dropout rate(1) = 0.025, dropout rate(2) = 0.025, dropout time = 12.

Stage 1 2 3
Information rate 53% 73.6% 100%
Efficacy boundary (z-value scale) 2.867 2.393 2.011
Overall power 0.2097 0.5198 0.8004
Expected number of subjects 1000.0
Number of subjects 1000.0 1000.0 1000.0
Expected number of events 317.2
Cumulative number of events 205.0 285.0 387.0
Analysis time 40.6 51.9 69.1
Expected study duration 57.8
Cumulative alpha spent 0.0021 0.0090 0.0250
One-sided local significance level 0.0021 0.0084 0.0222
Efficacy boundary (t) 0.670 0.753 0.815
Exit probability for efficacy (under H0) 0.0021 0.0069
Exit probability for efficacy (under H1) 0.2097 0.3101

Legend:

• (t): treatment effect scale
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