adapt is a function that performs adaptations and plans the secondary group sequential trial. The
effect size used for planning the secondary trial is a weighted mean between the interim estimate theta and
the initially assumed estimate delta (pT$delta) of the primary trial.
interim data; a list with the variables T and z; list(T = stage of interim analysis, z = interim z-statistic)
SF
spending function for the secondary trial
phi
parameter of spending function for the secondary trial when SF=3 or 4 (See below)
cp
conditional power
theta
new effect size (default: estimate from interim analysis)
I2min
minimal total information of secondary trial
I2max
maximal total information of secondary trial
swImax
maximal incremental information per stage
delta
initially assumed effect size for the primary trial (default: estimate from primary trial)
weight
weight of theta when updating the effect size estimate as weighted mean of theta and delta
warn
option if warnings should be printed to the screen (default: true)
Details
If no adaptation is performed then this indicates that the original plan is kept. In this case sT is set to NULL.
If an adaptation is performed sT is a list which contains the following elements:
K
number of stages
a
lower critical bounds of secondary group sequential design(are currently always set to -8)
b
upper critical bounds of secondary group sequential design
t
vector with cumulative information fractions
al
alpha (type I error rate); equal to the conditional type I error rate of the primary trial
SF
spending function
phi
parameter of spending function when SF=3 or 4 (See below)
alab
alpha-absorbing parameter values of secondary group sequential design
als
alpha-values ''spent'' at each stage of secondary group sequential design
Imax
maximum information number
delta
effect size used for planning the secondary trial
cp
conditional power
A value of SF=3 is the power family. Here, the spending function is t^{φ},
where phi must be greater than 0. A value of SF=4 is the Hwang-Shih-DeCani family,
with the spending function (1-e^{-φ t})/(1-e^{-φ}), where phi cannot be 0.
Value
adapt returns an object of the classGSTobj; the design of the secondary trial. The adaptation rule is as in the first simulation example of Brannath et al.(2008). If no adaptations are performed, the function returns sT = NULL. An object of classGSTobj is a list containing the following components:
Brannath, W, Mehta, CR, Posch, M (2008) ”Exact confidence bounds following adaptive group sequential tests”, Biometrics accepted.
See Also
GSTobj, print.GSTobj, plot.GSTobj, plan.GST
Examples
##The following performs an adaptation of the sample size and
##number of interim analyses after the first stage of the primary trial.
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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(AGSDest)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AGSDest/adapt.Rd_%03d_medium.png", width=480, height=480)
> ### Name: adapt
> ### Title: Adaptations in group sequential trials
> ### Aliases: adapt
> ### Keywords: methods
>
> ### ** Examples
>
> ##The following performs an adaptation of the sample size and
> ##number of interim analyses after the first stage of the primary trial.
>
> 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)
>
>
>
>
>
> dev.off()
null device
1
>