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rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

Summary

This R Markdown document provides an example for planning a trial with a binary endpoint using rpact. It also illustrates the use of ggplot2 for illustrating the characteristics of a sample size recalculation strategy. Another example for planning a trial with binary endpoints can be found here.

1 Designing a trial with binary endpoints

First, load the rpact package

library(rpact)
packageVersion("rpact") # version should be version 2.0.5 or later
## [1] '3.2.1'

Suppose a trial should be conducted in 3 stages where at the first stage 50%, at the second stage 75%, and at the final stage 100% of the information should be observed. Oâ€™Brien & Fleming boundaries should be used with one-sided $$\alpha = 0.025$$ and non-binding futility bounds 0 and 0.5 for the first and the second stage, respectively, on the $$z$$-value scale.

The endpoints are binary (failure rates) and should be compared in a parallel group design, i.e., the null hypothesis to be tested is $$H_0:\pi_1 - \pi_2 = 0\,,$$ which is tested against the alternative $$H_1: \pi_1 - \pi_2 < 0\,.$$

1.1 Sample size calculation

The necessary sample size to achieve 90% power if the failure rates are assumed to be $$\pi_1 = 0.40$$ and $$\pi_2 = 0.60$$ can be obtained as follows:

dGS <- getDesignGroupSequential(
informationRates = c(0.5, 0.75, 1), alpha = 0.025, beta = 0.1,
futilityBounds = c(0, 0.5)
)
r <- getSampleSizeRates(dGS, pi1 = 0.4, pi2 = 0.6)

The summary() command creates a nice table for the study design parameters:

summary(r)
## Sample size calculation for a binary 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 test for rates
## (normal approximation),
## H0: pi(1) - pi(2) = 0, H1; treatment rate pi(1) = 0.4, control rate pi(2) = 0.6,
## power 90%.
##
## Stage                                         1      2      3
## Information rate                            50%    75%   100%
## Efficacy boundary (z-value scale)         2.863  2.337  2.024
## Futility boundary (z-value scale)             0  0.500
## Overall power                            0.2958 0.6998 0.9000
## Expected number of subjects               198.3
## Number of subjects                        133.1  199.7  266.3
## Cumulative alpha spent                   0.0021 0.0105 0.0250
## One-sided local significance level       0.0021 0.0097 0.0215
## Efficacy boundary (t)                    -0.248 -0.165 -0.124
## Futility boundary (t)                     0.000 -0.035
## Overall exit probability (under H0)      0.5021 0.2275
## Overall exit probability (under H1)      0.3058 0.4095
## Exit probability for efficacy (under H0) 0.0021 0.0083
## Exit probability for efficacy (under H1) 0.2958 0.4040
## Exit probability for futility (under H0) 0.5000 0.2191
## Exit probability for futility (under H1) 0.0100 0.0056
##
## Legend:
##   (t): treatment effect scale

Note that the calculation of the efficacy boundaries on the approximate treatment effect scale is performed under the assumption that $$\pi_2 = 0.60$$ is the observed failure rate in the control group and states the treatment difference to be observed in order to reach significance (or stop the trial due to futility).

1.2 Optimum allocation ratio

The optimum allocation ratio yields the smallest overall sample size and depends on the choice of $$\pi_1$$ and $$\pi_2$$. It can be obtained by specifying allocationRatioPlanned = 0. In our case, the optimum allocation ratio is 1 but calculated numerically, therefore slightly unequal 1:

r <- getSampleSizeRates(dGS, pi1 = 0.4, pi2 = 0.6, allocationRatioPlanned = 0)
r$allocationRatioPlanned ## [1] 0.9999976 round(r$allocationRatioPlanned, 5)
## [1] 1

1.3 Boundary plots

The decision boundaries can be illustrated on different scales.

On the $$z$$-value scale:

plot(r, type = 1)

On the effect size scale:

plot(r, type = 2)

On the $$p$$-value scale:

plot(r, type = 3)