GSTobj: Group sequential trial object (GSTobj)

In AGSDest: Estimation in Adaptive Group Sequential Trials

Group sequential trial object (GSTobj)

Description

The GSTobj includes design and outcome of primary trial.

Usage

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, ...)

Arguments

x	object of the class GSTobj
...	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 class GSTobj
ctype	confidence type: repeated "r" or stage-wise ordering "so" (default: c("r", "so"))
ptype	p-value type: repeated "r" or stage-wise 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 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 stage-wise ordering

The calculated confidence bounds are saved as:

cb.r	repeated confidence bound
cb.so	confidence bound based on the stage-wise ordering

ptype defines the type of p-value that is calculated.

"r"	Repeated p-value for a classical GSD
"so"	Stage-wise adjusted p-value for a classical GSD

The calculated p-values are saved as:

pvalue.r	repeated p-value
pvalue.so	stage-wise adjusted p-value

etype defines the type of point estimate

"ml"	maximum likelihood estimate (ignoring the sequential nature of the design)
"mu"	median unbiased estimate (stage-wise 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 stage-wise adjusted confidence interval and p-value 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 p-value, 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 p-value at any stage of the trial the user has to specify the outcome GSDo in the object GSTobj (see example below).

Value

An object of class GSTobj, is basically a list with the elements

cb.so	confidence bound based on the stage-wise ordering
cb.r	repeated confidence bound
pvalue.so	stage-wise adjusted p-value
pvalue.r	repeated p-value
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	alpha-absorbing parameter values of group sequential design
als	alpha-values ”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	z-statistic at stage where trial stops

Note

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 Hwang-Shih-DeCani family.(1-e^{-φ t})/(1-e^{-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 Hwang-Shih-DeCani family, with the spending function (1-e^{-φ t})/(1-e^{-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)

Author(s)

Niklas Hack [e-mail] and Werner Brannath [e-mail]

See Also

GSTobj, print.GSTobj, plot.GSTobj, summary.GSTobj

Examples

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, p-value 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 stage-wise 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)

AGSDest documentation built on March 18, 2022, 6:30 p.m.