Description Usage Arguments Details Value Author(s) References See Also Examples

Based on a pre-planned graphical multiple comparison procedure, construct a valid multiple level alpha test that conserves the family wise error in the strong sense regardless of any trial adaptations during an unblinded interim analysis. - Implementation of adaptive procedures is still in an early stage and may change in the near future

1 2 | ```
secondStageTest(interim, select, matchCE = TRUE, zWeights = "reject",
G2 = interim@preplanned)
``` |

`interim` |
An object of class |

`select` |
A logical vector giving specifying which hypotheses are carried forward to the second stage |

`matchCE` |
Logical specifying whether second stage weights should be computed proportional to corresponding PCEs |

`zWeights` |
Either "reject","accept", or "strict" giving the rule what should be done in cases where none of the selected hypotheses has positive second stage weight. |

`G2` |
An object of class |

For details see the given references.

A function of signature `function(z2)`

with arguments
`z2`

a numeric vector with second stage z-scores (Z-scores of
dropped hypotheses should be set no `NA`

)
that returns objects of class `gMCPResult`

.

Florian Klinglmueller [email protected]

Frank Bretz, Willi Maurer, Werner Brannath, Martin Posch: A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine 2009 vol. 28 issue 4 page 586-604. http://www.meduniwien.ac.at/fwf_adaptive/papers/bretz_2009_22.pdf

Bretz F., Posch M., Glimm E., Klinglmueller F., Maurer W., Rohmeyer K. (2011): Graphical approaches for multiple endpoint problems using weighted Bonferroni, Simes or parametric tests - to appear.

Posch M, Futschik A (2008): A Uniform Improvement of Bonferroni-Type Tests by Sequential Tests JASA 103/481, 299-308

Posch M, Maurer W, Bretz F (2010): Type I error rate control in adaptive designs for confirmatory clinical trials with treatment selection at interim Pharm Stat 10/2, 96-104

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## Simple successive graph (Maurer et al. 2011)
## two treatments two hierarchically ordered endpoints
a <- .025
G <- simpleSuccessiveI()
## some z-scores:
p1=c(.1,.12,.21,.16)
z1 <- qnorm(1-p1)
p2=c(.04,1,.14,1)
z2 <- qnorm(1-p2)
v <- c(1/2,1/3,1/2,1/3)
intA <- doInterim(G,z1,v)
## select only the first treatment
fTest <- secondStageTest(intA,c(1,0,1,0))
``` |

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