Adaptive group sequential trial object (AGSTobj)
Description
The AGSTobj
includes design and outcome of primary and secondary trial.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  AGSTobj(x, ...)
## S3 method for class 'AGSTobj'
plot(x, main = c("primary trial", "secondary trial"),
print.pdf = FALSE, ...)
## S3 method for class 'AGSTobj'
print(x, ...)
## S3 method for class 'AGSTobj'
summary(object, ctype = c("r", "so"), ptype = c("r",
"so"), etype = c("ml", "mu", "cons"), overwrite = FALSE, ...)
## S3 method for class 'summary.AGSTobj'
print(x, ...)

Arguments
x 
object of the 
... 
additional arguments. 
main 
Title of the plots (default: first plot: "primary trial"; second plot: "secondary trial") 
print.pdf 
option; if TRUE a pdf file is created. Instead of setting print.pdf to TRUE, the user can specify a character string giving the name or the path of the file. 
object 
object of the 
ctype 
confidence type: repeated "r" or stagewise ordering "so" (default: c("r", "so")) 
ptype 
pvalue type: repeated "r" or stagewise ordering "so" (default: c("r", "so")) 
etype 
point estimate: maximum likelihood "ml", median unbiased "mu", or conservative "cons" (default: c("ml", "mu", "cons")) 
overwrite 
option; if TRUE all old values are deleted and new values are calculated (default: FALSE) 
Details
A AGSTobj
object is designed.
The function summary
returns an object of class
AGSTobj
.
ctype
defines the type of confidence interval that is calculated.
"r"  Repeated confidence bound for a GSD with design adaptations 
"so"  Confidence bound for a GSD with design adaptation based on the stagewise ordering 
The calculated confidence bounds are saved as:
cb.r  repeated confidence bound 
cb.so  confidence bound based on the stagewise ordering 
ptype
defines the type of pvalue that is calculated.
"r"  Repeated pvalue for a GSD with design adaptations 
"so"  Stagewise adjusted pvalue for a GSD with design adaptations 
' 
The calculated pvalues are saved as:
pvalue.r  repeated pvalue 
pvalue.so  stagewise adjusted pvalue 
etype
defines the type of point estimate
"ml"  maximum likelihood estimate (ignoring the sequential and adaptive nature of the design) 
"mu"  median unbiased estimate (stagewise lower confidence bound at level 0.5) for a GSD with design adaptations 
"cons"  conservative estimate (repeated lower confidence bound at level 0.5) for a GSD with design adaptations 
The calculated point estimates are saved as:
est.ml  Maximum likelihood estimate 
est.mu  Median unbiased estimate 
est.cons  Conservative estimate 
The stagewise adjusted confidence bound, pvalue and the median unbiased point estimate can only be calculated at the stage where the trial stops and are only valid if the stopping rule is met.
The repeated confidence bound, repeated pvalue, conservative estimate and maximum likelihood estimate can be calculated at every stage of the trial and
not just at the stage where the trial stops and are also valid if the stopping rule is not met.
For calculating the repeated confidence bounds or pvalues the user has to specify sTo
(secondary trial outcome) in the object AGSTobj
(see example below). If the stopping rule is not met in object sTo
then sta ewise adjusted confidence bounds and pvalues will not be computed while a warning message is given when their computation have erroneously been specified.
Value
An object of class AGSTobj
, which is basically a list with the elements
cb.so 
confidence bound based on the stagewise ordering (stagewise adjusted confidence bound) 
cb.r 
repeated confidence bound 
pvalue.so 
pvalue based on the stagewise ordering (stagewise adjusted pvalue) 
pvalue.r 
repeated pvalue 
est.ml 
maximum likelihood estimate 
est.mu 
median unbiased point estimate 
est.cons 
conservative point estimate 
pT 

K 
number of stages 
al 
alpha (type I error rate) 
a 
lower critical bounds of primary group sequential design (are currently always set to 8) 
b 
upper critical bounds of primary group sequential design 
t 
vector with cumulative information fraction 
SF 
spending function (for details see below) 
phi 
parameter of spending function when SF=3 or 4 (for details see below) 
alab 
alphaabsorbing parameter values of primary group sequential design 
als 
alphavalues ”spent” at each stage of primary group sequential design 
Imax 
maximum information number 
delta 
effect size used for planning the primary trial 
cp 
conditional power for planning the primary trial 
iD 

L 
stage of the adaptation 
z 
zstatistic at adaptive interim analysis 
sT 

K 
number of stages 
al 
conditional rejection probability 
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 fraction 
SF 
spending function (for details see below) 
phi 
parameter of spending function when SF=3 or 4 (for details see below) 
Imax 
maximum information number 
delta 
effect size used for planning the secondary trial 
cp 
conditional power for planning the secondary trial 
sTo 

T 
stage where trial stops 
z 
zstatistic at stage where trial stops 
Note
The AGSTobj
should always have the same ordering and names as given in the list above or as given in the example.
1. pt
, 2. iD
, 3. sT
, 4. sTo
SF
defines the spending function.
SF
= 1 O'Brien and Fleming type spending function of Lan and DeMets (1983)
SF
= 2 Pocock type spending function of Lan and DeMets (1983)
SF
= 3 Power family (c_α* t^φ); phi must be greater than 0.
SF
= 4 HwangShihDeCani family;(1e^{φ t})/(1e^{φ}), where phi
cannot be 0.
A value of SF
=3 corresponds to the power family. Here, the spending function is t^{φ},
where phi must be greater than 0. A value of SF
=4 corresponds to the HwangShihDeCani family,
with the spending function (1e^{φ t})/(1e^{φ}), where phi cannot be 0.
If a path is specified for print.pdf
, all \ must be changed to /. If a filename is specified the ending of the file must be (.pdf).
In the current version the vector of lower bounds a
should be set to rep(8,K)
Author(s)
Niklas Hack niklas.hack@meduniwien.ac.at and Werner Brannath werner.brannath@meduniwien.ac.at
See Also
AGSTobj
, print.AGSTobj
, plot.AGSTobj
, summary.AGSTobj
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  ## Not run:
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)
AGST
plot(AGST)
AGST<summary(AGST)
plot(AGST)
##The repeated confidence interval and pvalue at an earlier stage
##than the one where the trial stops (T=3).
summary(as.AGST(pT,iD,sT,sTo=list(T=1,z=1.7)),ctype="r",ptype="r")
##If the stagewise adjusted confidence interval is calculated at this stage,
##the function returns an error message
summary(as.AGST(pT,iD,sT,sTo=list(T=1,z=1.7)),ctype="so",ptype="so")
## End(Not run)
