This R Markdown document provides examples how to analyse a group sequential trial with a survival endpoint and provide inference throughout and at the end of the trial with rpact.

This tutorial provides two examples:

- The
**first example**illustrates**how to get inference (point estimate, confidence interval, and \(p\)-value) which respect the group sequential design after rejecting the null hypothesis at the interim or final analysis**. - The
**second example**illustrates tools which are relevant for**monitoring interim results of an ongoing trial**:**repeated confidence intervals**and**conditional power**.

For a general introduction to “Inference in group sequential designs”, please refer to the book “Group Sequential and Confirmatory Adaptive Designs in Clinical Trials” by Gernot Wassmer & Werner Brannath.

This tutorial only covers survival endpoints. Code for other
endpoints is similar but the dataset needs to be provided in a different
format (see `?getDataset`

for details).

For details about the Gallium trial, we refer to the primary study publication: Marcus et al, N Engl J Med 2017; 377:1331-1344.

**Trial characteristics**:

- Population: Treatment-naive follicular lymphoma patients.
- Comparison: Rituximab + chemotherapy vs. Obinutuzumab + chemotherapy. Rituximab: Rituxan, Mabthera. Obinutuzumab: Gazyva(ro).
- Phase III, 1:1 randomized, open-label clinical trial.
- Primary endpoint: investigator-assessed progression-free survival (PFS).

**Group-sequential design**:

- O’Brien & Fleming boundary with interim analyses after 30% and 67% of PFS events.
- Non-binding futility after 30% of PFS events (if estimated HR > 1).
- Target HR 0.74, 80% power at two-sided 5% significance level \(\Rightarrow\) final analysis at 370 PFS events.
- Target sample size is 1202 subjects.

Results from standard inference at the **futility interim
analysis after 113 events**:

- Stratified HR 0.69 (95% CI 0.47 to 1.01). \(\Rightarrow\)
**Trial continues.** - log(HR) = log(0.69) with standard error 0.20.
- Corresponding Z-score: log(0.69)/0.20 = -1.86.

Results from standard inference at the **efficacy interim
analysis after 245 events**:

- Stratified HR 0.66 (95% CI 0.51 to 0.85).
- log(HR) = log(0.66) with standard error 0.13.
- Corresponding Z-score: log(0.66)/0.13 = -3.225.
- Two-sided \(p\)-value is 0.0012
which was smaller than the critical value from the O’Brien-Fleming
boundary of 0.012. \(\Rightarrow\)
**Trial stopped early for efficacy**.

First, load the rpact package and
**define the group sequential boundaries** using the
function `getDesignGroupSequential`

. Note that while the
Gallium protocol specified a two-sided significance level of 5%, we
implement this via a one-sided significance level of 2.5% as rpact (sensibly)
only supports one-sided designs if futility interim analyses are
specified.

**First, load the rpact package **

```
library(rpact)
packageVersion("rpact") # version should be version 2.0.5 or later
```

`## [1] '3.3.2'`

and define the design:

```
# FutilityBounds = c(0,-6) are on the Z-scale; a value of Z = 0 implies futility
# if the interim estimate is "in the wrong direction" (i.e., HR >= 1 here),
# a value of Z = -6 is essentially the same as Z = -Inf and implies no futility
# boundary for the second interim as per the Gallium design
<- getDesignGroupSequential(
design informationRates = c(113, 245, 370) / 370,
typeOfDesign = "asOF", sided = 1, alpha = 0.025,
futilityBounds = c(0, -6), bindingFutility = FALSE
)
```

Note that `bindingFutility = FALSE`

has no impact because
it is the default, so actually this could be omitted (same holds for
`sided = 1`

and `alpha = 0.025`

).

Second, the **results after the first and second
interim** are specified using the function
`getDataset()`

:

```
# overallLogRanks: One-sided logrank statistic or Z-score ( = log(HR)/SE) from Cox regression
<- getDataSet(
results overallEvents = c(113, 245),
overallLogRanks = c(-1.86, -3.225),
overallAllocationRatio = c(1, 1)
)
```

Since rpact version 3.2, the prefix cumulative[Capital case of first letter of variable name]… or cum[Capital case of first letter of variable name]… can alternatively be used for this. That is,

```
<- getDataSet(
results cumulativeEvents = c(113, 245),
cumulativeLogRanks = c(-1.86, -3.225),
cumulativeAllocationRatio = c(1, 1)
)
```

defines the same survival dataset. This is used for creating the
**adjusted inference** using the function
`getAnalysisResults()`

(`directionUpper = FALSE`

is specified because the power is directed towards negative values of
the logrank statistics):

```
<- getAnalysisResults(
adj_result design = design,
dataInput = results,
directionUpper = FALSE
)kable(adj_result)
```

**Analysis results (survival of 2 groups, group sequential
design)**

**Design parameters**

*Information rates*: 0.305, 0.662, 1.000*Critical values*: 3.891, 2.520, 1.992*Futility bounds (non-binding)*: 0.000, -Inf*Cumulative alpha spending*: 0.00004995, 0.00587877, 0.02499999*Local one-sided significance levels*: 0.00004995, 0.00586101, 0.02318178*Significance level*: 0.0250*Test*: one-sided

**User defined parameters**

*Direction upper*: FALSE

**Default parameters**

*Normal approximation*: TRUE*Theta H0*: 1

**Stage results**

*Cumulative effect sizes*: 0.7047, 0.6623, NA*Stage-wise test statistics*: -1.860, -2.673, NA*Stage-wise p-values*: 0.031443, 0.003762, NA*Overall test statistics*: -1.860, -3.225, NA*Overall p-values*: 0.0314428, 0.0006299, NA

**Analysis results**

*Actions*: continue, reject and stop, NA*Conditional rejection probability*: 0.1373, 0.8616, NA*Conditional power*: NA, NA, NA*Repeated confidence intervals (lower)*: 0.3389, 0.4799, NA*Repeated confidence intervals (upper)*: 1.4653, 0.9139, NA*Repeated p-values*: 0.234459, 0.005409, NA*Final stage*: 2*Final p-value*: NA, 0.0006656, NA*Final CIs (lower)*: NA, 0.5157, NA*Final CIs (upper)*: NA, 0.8515, NA*Median unbiased estimate*: NA, 0.6626, NA

The output is explained as follows:

`Critical values`

are group sequential efficacy boundary values on the \(z\)-scale,`Local one-sided significance levels`

are the corresponding one-sided local significance levels.`Cumulative effect sizes`

refer to hazard ratio estimates that are based on the overall test statistics.`Test statistics`

and`p-values`

refer to \(z\)-scores and \(p\)-values obtained from the first interim analysis and results which would have been obtained after the second interim analysis if not all data up to the second interim analysis but only new data since the first interim had been included (i.e., per-stage results).`Overall test statistics`

are the given (overall, not per-stage) \(z\)-scores from each interim and`Overall p-value`

the corresponding one-sided \(p\)-values.`Repeated confidence intervals`

provide valid (but conservative) inference at any stage of an ongoing or stopped group sequential trial.`Repeated p-values`

are the corresponding \(p\)-values.`Final p-value`

is the**final one-sided adjusted \(p\)-value**based on the stagewise ordering of the sample space.`Median unbiased estimate`

and`Final CIs`

are the corresponding median-unbiased**adjusted treatment effect estimate**and the**confidence interval**for the hazard ratio at the interim analysis where the trial was stopped.

These results can also be displayed with the `summary()`

function:

`kable(summary(adj_result))`

**Analysis results for a survival endpoint**

Sequential analysis with 3 looks (group sequential design). The results were calculated using a two-sample logrank test (one-sided). H0: hazard ratio = 1 against H1: hazard ratio < 1.

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

Fixed weight | 0.305 | 0.662 | 1 |

Efficacy boundary (z-value scale) | 3.891 | 2.520 | 1.992 |

Futility boundary (z-value scale) | 0 | -Inf | |

Cumulative alpha spent | <0.0001 | 0.0059 | 0.0250 |

Stage level | <0.0001 | 0.0059 | 0.0232 |

Cumulative effect size | 0.705 | 0.662 | |

Overall test statistic | -1.860 | -3.225 | |

Overall p-value | 0.0314 | 0.0006 | |

Test action | continue | reject and stop | |

Conditional rejection probability | 0.1373 | 0.8616 | |

95% repeated confidence interval | [0.339; 1.465] | [0.480; 0.914] | |

Repeated p-value | 0.2345 | 0.0054 | |

Final p-value | 0.0007 | ||

Final confidence interval | [0.516; 0.852] | ||

Median unbiased estimate | 0.663 |

Note that for this example, the **adjusted final hazard ratio
of 0.663 and the adjusted confidence interval of (0.516, 0.852) match
the results from the conventional analysis almost exactly for the first
two decimals.** This is consistent with the finding that stopping
a trial after 50% or more of the events had been collected has a
negligible impact on estimation.

Monitoring ongoing trials is also possible with the function
`getAnalysisResults`

introduced above. **Repeated
confidence intervals** which provide valid (but conservative)
inference at any stage of an ongoing or stopped group sequential trial
can be obtained using the same code as introduced in the previous
example. **Conditional power calculations** require
additional specification of the following arguments:

- The assumed true hazard ratio
`thetaH1`

. - The planned number of additional events for future interim stages
(
`nPlanned`

). - The planned allocation ratio
`allocationRatioPlanned`

for future interim stages (default is 1).

We illustrate these capabilities by introducing **hypothetical
interim results** for the Gallium trial.

Assume the same design as for the Gallium trial introduced above and the following interim results:

**Hypothetical results** from standard inference at the
**futility interim analysis** after 113 events:

- Stratified HR 0.69 (95% CI 0.47 to 1.01). \(\Rightarrow\)
**Trial would continue.** - log(HR) = log(0.69) with standard error 0.20.
- Corresponding Z-score: log(0.69)/0.20 = -1.86.

**Hypothetical results** from standard inference at the
**efficacy interim analysis** after 245 events:

- Stratified HR 0.80 (95% CI 0.62 to 1.03). \(\Rightarrow\)
**Trial would continue.** - log(HR) = log(0.80) with standard error 0.13.
- Corresponding Z-score: log(0.80)/0.13 = -1.716.

**Calculation of repeated confidence intervals** and
**conditional power**:

```
# 1) Specify results so far using function getDataset as before
<- getDataset(
results cumulativeEvents = c(113, 245),
cumulativeLogRanks = c(-1.86, -1.716),
cumulativeAllocationRatio = c(1, 1)
)
# 2) Calculate repeated confidence intervals and conditional power using
# the function getAnalysisResults as before
# Additional arguments for the conditional power calculation are
# - nPlanned: additional events from second interim until final analysis
# (370-245 for this trial)
# - thetaH1: True hazard ratio governing future stages
# (set to 0.74 here as per the original protocol assumptions)
```