gMCP: secondStageTest – R documentation

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secondStageTest

EXPERIMENTAL: Construct a valid level alpha test for the second stage of an adaptive design that is based on a pre-planned graphical MCP

Description

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

Usage

secondStageTest(interim, select, matchCE = TRUE, zWeights = "reject",
  G2 = interim@preplanned)

Arguments

`interim`	An object of class `gPADInterim`.
`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 `graphMCP` laying down the rule to compute second stage weights. Defaults to pre-planned graph.

Details

For details see the given references.

Value

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.

Author(s)

Florian Klinglmueller float@lefant.net

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. 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

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))

gMCP

Graph Based Multiple Comparison Procedures

v0.8-15

GPL (>= 2)

Authors

Kornelius Rohmeyer [aut, cre], Florian Klinglmueller [aut]

Initial release