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

Computes partial conditional errors (PCE) for a pre-planned graphical procedure given information fractions and first stage z-scores. - Implementation of adaptive procedures is still in an early stage and may change in the near future

1 | ```
doInterim(graph, z1, v, alpha = 0.025)
``` |

`graph` |
A graph of class |

`z1` |
A numeric vector giving first stage z-scores. |

`v` |
A numeric vector giving the proportions of pre-planned measurements collected up to the interim analysis. Will be recycled of length different than the number of elementary hypotheses. |

`alpha` |
A numeric specifying the maximal allowed type one error rate. |

For details see the given references.

An object of class `gPADInterim`

, more specifically a list with
elements

`Aj`

a matrix of PCEs for all elementary hypotheses in each intersection hypothesis

`BJ`

a numeric vector giving sum of PCEs per intersection hypothesis

`preplanned`

Pre planned test represented by an object of class

`graphMCP`

Florian Klinglmueller float@lefant.net

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

Frank Bretz, Martin Posch, Ekkehard Glimm, Florian Klinglmueller, Willi Maurer, Kornelius Rohmeyer (2011): Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes or parametric tests. Biometrical Journal 53 (6), pages 894-913, Wiley. http://onlinelibrary.wiley.com/doi/10.1002/bimj.201000239/full

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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