secondStageTest | R Documentation |
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
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 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. 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
graphMCP
, doInterim
## 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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