EXPERIMENTAL: Evaluate conditional errors at interim for a pre-planned graphical procedure
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
doInterim(graph, z1, v, alpha = 0.025)
A graph of class
A numeric vector giving first stage z-scores.
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.
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
a matrix of PCEs for all elementary hypotheses in each intersection hypothesis
a numeric vector giving sum of PCEs per intersection hypothesis
Pre planned test represented by an object of class
Florian Klinglmueller firstname.lastname@example.org
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
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## 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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