This R Markdown document provides an example that illustrates how to compare sample size and power calculation results of the two different R packages rpact and gsDesign.

- 1:1 randomized
- Two-sided log-rank test; 80% power at the 5% significance level (or one-sided at 2.5%)
- Target HR for primary endpoint (PFS) is 0.75
- PFS in the control arm follows a piece-wise exponential
distribution, with the hazard rate h(t) estimated using historical
controls as follows:
- h(t) = 0.025 for t between 0 and 6 months;
- h(t) = 0.04 for t between 6 and 9 months;
- h(t) = 0.015 for t between 9 and 15 months;
- h(t) = 0.01 for t between 15 and 21 months;
- h(t) = 0.007 for t beyond 21 months.

- An annual dropout probability of 20%
- Interim analyses at 33% and 70% of total information
- Alpha-spending version of O’Brien-Fleming boundary for efficacy
- No futility interim
- 1405 subjects recruited in total
- Staggered recruitment:
- 15 pt/month during first 12 months;
- subsequently, increase of # of sites and ramp up of recruitment by +6 pt/month each month until a maximum of 45 pt/month

```
# Load the package `gsDesign`
library(gsDesign)
options(warn = -1) # avoid warnings generated by gsDesign
<- gsSurv(
x k = 3, test.type = 1, alpha = 0.025, beta = 0.2,
timing = c(0.33, 0.7), sfu = sfLDOF, # boundary
hr = 0.75,
lambdaC = c(0.025, 0.04, 0.015, 0.01, 0.007), # piecewise lambdas
S = c(6, 3, 6, 6), # piecewise survival times
eta = -log(1 - 0.2) / 12, # dropout
gamma = c(15, 21, 27, 33, 39, 45), # recruitment, pt no
R = c(12, 1, 1, 1, 1, (1405 - 300) / 45), # recruitment duration
minfup = NULL
)print(x, digits = 5)
```

```
## Time to event group sequential design with HR= 0.75
## Equal randomization: ratio=1
## One-sided group sequential design with
## 80 % power and 2.5 % Type I Error.
##
## Analysis N Z Nominal p Spend
## 1 128 3.73 0.0001 0.0001
## 2 271 2.44 0.0074 0.0073
## 3 386 2.00 0.0227 0.0176
## Total 0.0250
##
## ++ alpha spending:
## Lan-DeMets O'Brien-Fleming approximation spending function with none = 1.
##
## Boundary crossing probabilities and expected sample size
## assume any cross stops the trial
##
## Upper boundary (power or Type I Error)
## Analysis
## Theta 1 2 3 Total E{N}
## 0.0000 0.0001 0.0073 0.0176 0.025 385.0
## 0.1437 0.0175 0.4517 0.3309 0.800 329.1
## T n Events HR efficacy
## IA 1 26.78703 785.4162 127.3407 0.516
## IA 2 38.62360 1318.0620 270.1171 0.743
## Final 50.80093 1405.0000 385.8810 0.816
## Accrual rates:
## Stratum 1
## 0-12 15
## 12-13 21
## 13-14 27
## 14-15 33
## 15-16 39
## 16-40.56 45
## Control event rates (H1):
## Stratum 1
## 0-6 0.025
## 6-9 0.040
## 9-15 0.015
## 15-21 0.010
## 21-Inf 0.007
## Censoring rates:
## Stratum 1
## 0-6 0.0186
## 6-9 0.0186
## 9-15 0.0186
## 15-21 0.0186
## 21-Inf 0.0186
```

```
# Load the package `rpact`
library(rpact)
packageVersion("rpact")
```

```
<- getDesignGroupSequential(
design sided = 1, alpha = 0.025, beta = 0.2,
informationRates = c(0.33, 0.7, 1),
typeOfDesign = "asOF"
)kable(summary(design))
```

**Sequential analysis with a maximum of 3 looks (group
sequential design)**

O’Brien & Fleming type alpha spending design, one-sided overall significance level 2.5%, power 80%, undefined endpoint, inflation factor 1.015.

Stage | 1 | 2 | 3 |
---|---|---|---|

Information rate | 33% | 70% | 100% |

Efficacy boundary (z-value scale) | 3.731 | 2.440 | 2.000 |

Stage Levels | <0.0001 | 0.0074 | 0.0227 |

Cumulative alpha spent | <0.0001 | 0.0074 | 0.0250 |

Overall power | 0.0175 | 0.4691 | 0.8000 |

```
<- list(
piecewiseSurvivalTime "0 - <6" = 0.025,
"6 - <9" = 0.04,
"9 - <15" = 0.015,
"15 - <21" = 0.01,
">= 21" = 0.007
)
<- list(
accrualTime "0 - <12" = 15,
"12 - <13" = 21,
"13 - <14" = 27,
"14 - <15" = 33,
"15 - <16" = 39,
">= 16" = 45
)
<- getPowerSurvival(
y design = design, typeOfComputation = "Schoenfeld",
thetaH0 = 1, directionUpper = FALSE,
dropoutRate1 = 0.2, dropoutRate2 = 0.2, dropoutTime = 12,
allocationRatioPlanned = 1,
accrualTime = accrualTime,
piecewiseSurvivalTime = piecewiseSurvivalTime,
hazardRatio = 0.75,
maxNumberOfEvents = x$n.I[3],
maxNumberOfSubjects = 1405
)kable(summary(y))
```

**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, piecewise survival distribution, piecewise survival time = c(0, 6, 9, 15, 21), control lambda(2) = c(0.025, 0.04, 0.015, 0.01, 0.007), maximum number of subjects = 1405, maximum number of events = 386, accrual time = c(12, 13, 14, 15, 16, 40.556), accrual intensity = c(15, 21, 27, 33, 39, 45), dropout rate(1) = 0.2, dropout rate(2) = 0.2, dropout time = 12.

Stage | 1 | 2 | 3 |
---|---|---|---|

Information rate | 33% | 70% | 100% |

Efficacy boundary (z-value scale) | 3.731 | 2.440 | 2.000 |

Overall power | 0.0175 | 0.4702 | 0.8009 |

Expected number of subjects | 1354.8 | ||

Number of subjects | 785.4 | 1318.1 | 1405.0 |

Expected number of events | 328.9 | ||

Cumulative number of events | 127.3 | 270.1 | 385.9 |

Expected study duration | 44.9 | ||

Cumulative alpha spent | <0.0001 | 0.0074 | 0.0250 |

One-sided local significance level | <0.0001 | 0.0074 | 0.0227 |

Efficacy boundary (t) | 0.516 | 0.743 | 0.816 |

Exit probability for efficacy (under H0) | <0.0001 | 0.0073 | |

Exit probability for efficacy (under H1) | 0.0175 | 0.4526 |

Legend:

*(t)*: treatment effect scale

Absolute differences:

```
<- as.data.frame(sprintf("%.5f", (x$T - y$analysisTime)))
timeDiff rownames(timeDiff) <- c("Stage 1", "Stage 2", "Stage 3")
colnames(timeDiff) <- "Difference analysis time"
kable(timeDiff)
```

Difference analysis time | |
---|---|

Stage 1 | -0.00000 |

Stage 2 | 0.00004 |

Stage 3 | -0.00011 |

Obviously, there is a difference in the calculation of the necessary number of events which are, in rpact, calculated as

```
qnorm(0.975) + qnorm(0.8))^2 / log(0.75)^2 * 4 *
(getDesignCharacteristics(getDesignGroupSequential(
sided = 1, alpha = 0.025,
kMax = 3, typeOfDesign = "asOF", informationRates = c(0.33, 0.7, 1)
$inflationFactor ))
```

`## [1] 385.0479`

which is slightly different to the maximum number of events in gsDesign which is

`$n.I[3] x`

`## [1] 385.881`

Therefore, running

```
getSampleSizeSurvival(
design = design, typeOfComputation = "Schoenfeld",
thetaH0 = 1,
dropoutRate1 = 0.2, dropoutRate2 = 0.2, dropoutTime = 12,
allocationRatioPlanned = 1,
```