This R markdown file 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] '2.0.5.9009'`

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. O’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 \(H_1: \pi_1 - \pi_2 < 0\,.\)

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:

```
## Sample size calculation for a binary endpoint
##
## Sequential analysis with a maximum of 3 looks (group sequential design).
## The sample size was calculated for a two-sample test for rates (one-sided),
## treatment rate pi (1) = 0.4, control rate pi (2) = 0.6, and 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
## Number of subjects 134 200 267
## Cumulative alpha spent 0.0021 0.0105 0.0250
## Cumulative power 0.2958 0.6998 0.9000
## 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): approximate 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).

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`

`## [1] 1`

The decision boundaries can be illustrated on different scales.

On the z-value scale:

On the effect size scale:

On the p-value scale:

Suppose that \(N = 280\) subjects were planned for the study. The power if the failure rate in the active treatment group is \(\pi_1 = 0.40\) or \(\pi_1 = 0.50\) can be achieved as follows:

```
power <- getPowerRates(dGS, maxNumberOfSubjects = 280, pi1 = c(0.4, 0.5), pi2 = 0.6,
directionUpper = FALSE)
power$overallReject
```

`## [1] 0.914045 0.377853`

Note that `directionUpper = FALSE`

is used because the study is powered for
alternatives \(\pi_1 - \pi_2\) being smaller than 0.

The power for \(\pi_1 = 0.50\) (37.8%) is much reduced as compared to the case \(\pi_1 = 0.40\) (where it exceeds 90%):

We also can graphically illustrate the power, the expected sample size, and the
early stopping and futility stopping probabilities for a range of alternative
values. This can be done by specifying the lower and the upper bound for \(\pi_1\)
in `getPowerRates()`

and use the generic `plot()`

command with `type = 6`

:

Suppose that, using an adaptive design, the sample size from the above example
can be increased *in the last interim* up to a 4-fold of the originally planned
sample size for the last stage. Conditional power 90% *based on the observed
effect sizes (failure rates)* should be used to increase the sample size. We
want to use the inverse normal method to allow for the sample size increase and
compare the test characteristics with the group sequential design from the above
example.

To assess the test characteristics of this adaptive design we first define the inverse normal design and then perform two simulations, one without and one with SSR:

```
dIN <- getDesignInverseNormal(informationRates = c(0.5,0.75,1),
alpha = 0.025, beta = 0.1, futilityBounds = c(0,0.5))
maxiter <- 1000
seed <- 12345
sim1 <- getSimulationRates(dIN, plannedSubjects = c(140,210,280),
pi1 = seq(0.4,0.5,0.01), pi2 = 0.6, directionUpper = FALSE,
maxNumberOfIterations = maxiter, conditionalPower = 0.9,
minNumberOfSubjectsPerStage = c(140,70,70),
maxNumberOfSubjectsPerStage = c(140,70,70), seed = seed)
sim2 <- getSimulationRates(dIN, plannedSubjects = c(140,210,280),
pi1 = seq(0.4,0.5,0.01), pi2 = 0.6, directionUpper = FALSE,
maxNumberOfIterations = maxiter, conditionalPower = 0.9,
minNumberOfSubjectsPerStage = c(NA,70,70),
maxNumberOfSubjectsPerStage = c(NA,70,4*70), seed = seed)
```

Note that the sample sizes will be calculated under the assumption that the
*conditional power for the subsequent stage* is 90%. If the resulting sample
size is larger, the upper bound (4*70 = 280) is used.

Note also that `sim1`

can also be *calculated* using `getPowerRates()`

or can
also *easier be simulated* without specifying `conditionalPower`

,
`minNumberOfSubjectsPerStage`

, and `maxNumberOfSubjectsPerStage`

(which
obviously is redundant for `sim1`

) but this way ensures that the calculated
objects `sim1`

and `sim2`

*contain exactly the same parameters* and therefore
can easier be combined (see below).

We can look at the power of the two procedures and assess the power gain of using the adaptive design which comes along with an increase expected sample size:

`## [1] 0.40 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.50`

`## [1] 0.918 0.897 0.855 0.814 0.761 0.729 0.664 0.578 0.510 0.451 0.390`

`## [1] 0.973 0.965 0.944 0.939 0.881 0.859 0.793 0.739 0.677 0.589 0.514`

`## [1] 204.3 207.3 214.3 218.1 225.6 230.9 232.1 235.3 239.1 237.2 237.4`

`## [1] 237.8 250.3 263.4 277.4 287.7 307.4 318.2 331.6 337.2 336.8 351.8`

We now want to graphically illustrate the gain in power when using the adaptive
sample size recalculation. We use `ggplot2`

for doing this. First, a data set
`df`

combining `sim1`

and `sim2`

is defined with the additional variable SSR.
Defining `mytheme`

and using the following `ggplot2`

commands, the difference in
power and ASN of the two strategies is illustrated. It shows that at least for
(absolute) effect difference > 0.15 an overall power of more than around 85% can
be achieved with the proposed sample size recalculation strategy.

```
library(ggplot2)
dataSim1 <- as.data.frame(sim1, niceColumnNamesEnabled = FALSE)
dataSim2 <- as.data.frame(sim2, niceColumnNamesEnabled = FALSE)
dataSim1$SSR <- rep("no SSR", nrow(dataSim1))
dataSim2$SSR <- rep("SSR", nrow(dataSim2))
df <- rbind(dataSim1, dataSim2)
myTheme = theme(
axis.title.x = element_text(size = 12), axis.text.x = element_text(size = 12),
axis.title.y = element_text(size = 12), axis.text.y = element_text(size = 12),
plot.title = element_text(size = 14, hjust = 0.5),
plot.subtitle = element_text(size = 12, hjust = 0.5))
p <- ggplot(data = df,
aes(x = effect, y = overallReject, group = SSR, color = SSR)) +
geom_line(size = 1.1) +
geom_line(aes(x = effect, y = expectedNumberOfSubjects / 400,
group = SSR, color = SSR), size = 1.1, linetype = "dashed") +
scale_y_continuous("Power", sec.axis = sec_axis(~ . * 400, name = "ASN"),
limits = c(0.2, 1)) + xlab("effect") +
ggtitle("Power and ASN", "Power solid, ASN dashed") +
geom_hline(size = 0.5, yintercept = 0.8, linetype = "dotted") +
geom_hline(size = 0.5, yintercept = 0.9, linetype = "dotted") +
geom_vline(size = 0.5, xintercept = c(-0.2, -0.15), linetype = "dashed") +
theme_classic() + myTheme
plot(p)
```

For saving the graph, use

`ggplot2::ggsave(filename = "c:/yourdirectory/comparison.png",`

`plot = ggplot2::last_plot(), device = NULL, path = NULL,`

`scale = 1.2, width = 20, height = 12, units = "cm", dpi = 600,`

`limitsize = TRUE)`

For another example of using `ggplot2`

in rpact see also this vignette:
vignettes.rpact.org/html/rpact_ggplot_examples.html.

Finally, we create a histogram for the attained sample size of the study when using the adaptive sample size recalculation.

With the `getData()`

command the simulation results are obtained and
`str(simdata)`

provides information of the struture of this data:

```
## 'data.frame': 24391 obs. of 17 variables:
## $ iterationNumber : num 1 1 1 2 2 3 4 4 5 5 ...
## $ stageNumber : num 1 2 3 1 2 1 1 2 1 2 ...
## $ pi1 : num 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 ...
## $ pi2 : num 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 ...
## $ numberOfSubjects : num 140 70 280 140 70 140 140 70 140 70 ...
## $ rejectPerStage : num 0 0 1 0 1 1 0 1 0 1 ...
## $ futilityPerStage : num 0 0 0 0 0 0 0 0 0 0 ...
## $ testStatistic : num 1.18 1.11 2.03 0.34 2.35 ...
## $ testStatisticsPerStage : num 1.184 0.239 2.153 0.34 3.592 ...
## $ overallRates1 : num 0.429 0.438 0.449 0.429 0.371 ...
## $ overallRates2 : num 0.529 0.514 0.555 0.457 0.533 ...
## $ stagewiseRates1 : num 0.429 0.457 0.457 0.429 0.257 ...
## $ stagewiseRates2 : num 0.529 0.486 0.586 0.457 0.686 ...
## $ sampleSizesPerStage1 : num 70 70 70 70 70 70 70 70 70 70 ...
## $ sampleSizesPerStage2 : num 70 70 70 70 70 70 70 70 70 70 ...
## $ trialStop : logi FALSE FALSE TRUE FALSE TRUE TRUE ...
## $ conditionalPowerAchieved: num NA 0.061313 0.19483 NA 0.000438 ...
```

Depending on \(\pi_1\) (in this example, for \(\pi_1 = 0.5\)), you can create the histogram of the simulated total sample size as follows:

```
simDataPart <- simData[simData$pi1 == 0.5,]
overallSampleSizes <-
sapply(1:maxiter, function(i) {
sum(simDataPart[simDataPart$iterationNumber == i,]$numberOfSubjects)
})
hist(overallSampleSizes, main = "Histogram", xlab = "Achieved sample size")
```

How often the maximum and other sample sizes are reached over the stages can be obtained as follows:

```
subjectsRange <- cut(simDataPart$numberOfSubjects, c(69, 70, 139, 140, 210, 279, 280))
round(prop.table(table(simDataPart$stageNumber,subjectsRange), margin = 1)*100,1)
```

```
## subjectsRange
## (69,70] (70,139] (139,140] (140,210] (210,279] (279,280]
## 1 0.0 0.0 100.0 0.0 0.0 0.0
## 2 100.0 0.0 0.0 0.0 0.0 0.0
## 3 0.0 10.1 0.3 7.6 5.5 76.5
```

Gernot Wassmer and Werner Brannath,

*Group Sequential and Confirmatory Adaptive Designs in Clinical Trials*, Springer 2016, ISBN 978-3319325606R-Studio,

*Data Visualization with ggplot2 - Cheat Sheet*, version 2.1, 2016, https://www.rstudio.com/wp-content/uploads/2016/11/ggplot2-cheatsheet-2.1.pdf

System: rpact 2.0.5.9009, R version 3.6.1 (2019-07-05), platform: x86_64-w64-mingw32

To cite package ‘rpact’ in publications use:

Gernot Wassmer and Friedrich Pahlke (2019). rpact: Confirmatory Adaptive Clinical Trial Design and Analysis. R package version 2.0.5.9009. https://www.rpact.org

This work by Gernot Wassmer and Friedrich Pahlke is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.