gMCP (version 0.8-17)

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
)

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.

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.

Author

Florian Klinglmueller

Details

For details see the given references.

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

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

See Also

graphMCP, doInterim

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))