plan.GST: Plans a group sequential trial (GST)

In AGSDest: Estimation in Adaptive Group Sequential Trials

Plans a group sequential trial (GST)

Description

Plans a group sequential trial (GST)

Usage

plan.GST(K, t = (1:K)/K, Imax = NULL, SF, phi, alpha, delta = NULL,
  pow = NULL, compute.alab = TRUE, compute.als = TRUE)

Arguments

K	number of stages
t	vector with the cumulative information fraction (default: (1:K)/K)
Imax	maximum information number (default: NULL)
SF	spending function (for details see below)
phi	parameter of spending function when SF=3 or 4 (See below)
alpha	alpha (type I error rate)
delta	effect size (alternative)(default: NULL)
pow	power (default: NULL)
compute.alab	specify if alpha-absorbing parameter values should be calculated (default: TRUE)
compute.als	specify if alpha-values ”spent” at every stage should be calculated (default: TRUE)

Details

The user has to specify either Imax or delta and pow. If all three items are specified, the pre-defined maximum information number is newly calculated from the information for delta and power, and Imax is overwritten.

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

Value

plan.GST returns an object of the class GSTobj. An object of class GSTobj is a list containing the following components:

K	number of stages
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
al	alpha (type I error)
SF	spending function
phi	parameter of spending function when SF=3 or 4 (See below)
Imax	maximum information number
delta	effect size used for planning the primary trial

Author(s)

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

References

Brannath, W, Mehta, CR, Posch, M (2008) ”Exact confidence bounds following adaptive group sequential tests”, Biometrics accepted.

See Also

GSTobj, print.GSTobj, plot.GSTobj

Examples

##The following plans an O'Brien and Flaming group sequential design (GSD)
##with 4 stages and equally spaced looks.
pT <- plan.GST(K=4, SF=1, phi=0, alpha=0.025, delta=6, pow=0.8, compute.alab=TRUE, compute.als=TRUE)

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