Description Usage Arguments Details Value Note Author(s) See Also Examples
The GSTobj
includes design and outcome of primary trial.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  GSTobj(x, ...)
## S3 method for class 'GSTobj'
plot(x, main = "GSD", print.pdf = FALSE, ...)
## S3 method for class 'GSTobj'
print(x, ...)
## S3 method for class 'GSTobj'
summary(object, ctype = c("r", "so"), ptype = c("r", "so"),
etype = c("ml", "mu", "cons"), overwrite = FALSE, ...)
## S3 method for class 'summary.GSTobj'
print(x, ...)

x 
object of the 
... 
additional arguments. 
main 
Title of the plots (default: "GSD") 
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) 
A GSTobj
object is designed.
The function summary
returns an object of class
GSTobj
.
ctype
defines the type of confidence interval that is calculated.
"r"  Repeated confidence bound for a classical GSD 
"so"  Confidence bound for a classical GSD 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 classical GSD 
"so"  Stagewise adjusted pvalue for a classical GSD 
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 nature of the design) 
"mu"  median unbiased estimate (stagewise lower confidence bound at level 0.5) for a classical GSD 
"cons"  Conservative estimate (repeated lower confidence bound at level 0.5) for a classical GSD 
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 interval and pvalue and the median unbiased point estimate can only be calculated at the stage where the trial stops and is only valid if the stopping rule is met.
The repeated confidence interval and 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 is also valid if the stopping rule is not met.
For calculating the repeated confidence interval or pvalue at any stage of the trial the user has to specify the outcome GSDo
in the object GSTobj
(see example below).
An object of class GSTobj
, is basically a list with the elements
cb.so 
confidence bound based on the stagewise ordering 
cb.r 
repeated confidence bound 
pvalue.so 
stagewise adjusted pvalue 
pvalue.r 
repeated pvalue 
est.ml 
maximum likelihood estimate 
est.mu 
median unbiased point estimate 
est.cons 
conservative point estimate 
GSD 

K 
number of stages 
al 
alpha (type I error rate) 
a 
lower critical bounds of group sequential design (are currently always set to 8) 
b 
upper critical bounds of 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 group sequential design 
als 
alphavalues ”spent” at each stage of group sequential design 
Imax 
maximum information number 
delta 
effect size used for planning the primary trial 
cp 
conditionla power of the trial 
GSDo 

T 
stage where trial stops 
z 
zstatistic at stage where trial stops 
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 a
should be set to rep(8,K)
Niklas Hack [email protected] and Werner Brannath [email protected]
GSTobj
, print.GSTobj
, plot.GSTobj
, summary.GSTobj
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  GSD=plan.GST(K=4,SF=1,phi=0,alpha=0.025,delta=6,pow=0.8,compute.alab=TRUE,compute.als=TRUE)
GST<as.GST(GSD=GSD,GSDo=list(T=2, z=3.1))
GST
plot(GST)
GST<summary(GST)
plot(GST)
##The repeated confidence interval, pvalue and maximum likelihood estimate
##at the earlier stage T=1 where the trial stopping rule is not met.
summary(as.GST(GSD,GSDo=list(T=1,z=0.7)),ctype="r",ptype="r",etype="ml")
## Not run:
##If e.g. the stagewise adjusted confidence interval is calculated at this stage,
##the function returns an error message
summary(as.GST(GSD,GSDo=list(T=1,z=0.7)),ctype="so",etype="mu")
## End(Not run)

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