Estimation in adaptive group sequential trials
The package allows to compute repeated confidence intervals as well as confidence intervals based on the stage-wise ordering in groug sequential designs (GSD; see Jennison and Turnbull, 1989; Tsiatis, Rosner, Mehta, 1984) and adaptive groug sequential designs (Mehta, Bauer, Posch, Brannath, 2007; Brannath, Mehta, Posch, 2008). For adaptive group sequential designs the confidence intervals are based on the conditional rejection probability principle of Mueller and Schaefer (2001). This principle allows us to perform data dependent changes to the sample size, the spending function, and the number and spacing of interim looks while preserving the overall type I error rate. Currently the procedures do not support the use of futility boundaries as well as more than one adaptive interim analysis. Furthermore, the package is currently restricted to the computation of lower one-sided confidence intervals.
Type: Package Version: 2.2 Date: 2015-01-12 License: GPL Version 2 or later
adapt: Performs adaptations at an interim analysis of a GSD to the sample size, number of interim stages and spending function based on the conditional power in a GSD at an interim analysis; the result is a secondary trial
plan.GST: Plans a group sequential trial
cer: Computes the conditional type I error rate (also called conditional rejection probability) of a GSD at an interim analysis
typeIerr: Computes the type I error rate of a GSD
pvalue: Computes the repeated or stage-wise adjusted p-value for a classical GSD or for a GSD with design adaptations
seqconfint: Computes the repeated confidence bound and confidence bound based on the stage-wise ordering for a GSD or for a GSD with design adaptations
as.GST: Builds a group sequential trial object
as.AGST: Builds an adaptive group sequential trial object
Brannath, W, Mehta, CR, Posch, M (2008) ”Exact confidence bounds following adaptive group sequential tests”, Biometrics accepted.
Jennison, C, Turnbull, BW (1989) ”Repeated confidence intervals for group sequential clinical trials”, Contr. Clin. Trials, 5, 33-45.
Mehta, CR, Bauer, P, Posch, M, Brannath, W (2007) ”Repeated confidence intervals for adaptive group sequential trials”, Statistics in Medicine, 26, 5422-5433.
Mueller, HH, Schaefer, H (2001) ”Adaptive group sequential design for clinical trials: Combining the advantages of adaptive and of classical group sequential approaches”, Biometrics, 57, 886-891.
O'Brien, PC, Fleming, TR (1979) ”A multiple testing procedure for clinical trials”, Biometrics, 35 , 549-556
Schoenfeld, D (2001) ”A simple Algorithm for Designing Group Sequential Clinical Trials”, Biometrics, 27, 972-974
Tsiatis,AA, Rosner,GL, Mehta,CR (1984) ”Exact confidence intervals following a group sequential test”, Biometrics, 40, 797-804.
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pT=plan.GST(K=3,SF=4,phi=-4,alpha=0.05,delta=6,pow=0.9,compute.alab=TRUE,compute.als=TRUE) iD=list(T=1, z=1.090728) swImax=0.0625 I2min=3*swImax I2max=3*swImax sT=adapt(pT=pT,iD=iD,SF=1,phi=0,cp=0.8,theta=5,I2min,I2max,swImax) sTo=list(T=2, z=2.393) AGST <- as.AGST(pT=pT,iD=iD,sT=sT,sTo=sTo) ##The following calculates the stage-wise adjusted p-value ##of a group sequential trial after a design adaptation pvalue(AGST,type="so") ##and the corresponding confidence bound based on the stage-wise ordering. seqconfint(AGST,type="so") ##Both, the p-value and the confidence interval can be calculated by ##the summary function ## Not run: summary(AGST,ctype="so",ptype="so") ## End(Not run)
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