doInterim {gMCP}

EXPERIMENTAL: Evaluate conditional errors at interim for a pre-planned graphical procedure

Description

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

Usage

doInterim(graph, z1, v, alpha = 0.025)

Arguments

graph

A graph of class graphMCP.

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.

Details

For details see the given references.

Value

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

Author(s)

Florian Klinglmueller [e-mail]

References

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. https://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. doi:10.1002/bimj.201000239

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

See Also

graphMCP, secondStageTest

Examples

## 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))