optgs: Finding optimal and balanced group-sequential designs

Description

optgs is used to find a one-sided multi-stage design that balances four optimality criteria for a RCT with normally distributed outcomes

Usage

optgs(delta0 = 0, delta1 = 1/3, J = 2, sigma = 1, sd.known = TRUE, alpha = 0.05, power = 0.9, weights = c(0.95, 0, 0, 0.05), initial = NULL)

Arguments

delta0

mean difference in treatment effect under the null hypothesis (default: 0)

delta1

clinically relevant difference used to power the trial (default: 1/3)

number of stages in the trial (default: 2)

sigma

assumed standard deviation of treatment responses (default: 1)

sd.known

logical value indicating if sigma will be treated as known; if FALSE, a quantile substitution method will be used to modify the stopping boundaries (default TRUE)

alpha

one-sided type-I error rate required (default: 0.05)

power

power required (default: 0.9)

weights

vector of length 4 giving the weights put on the four optimality criteria (default: c(0.95,0,0,0.05)). See details for more information

initial

starting values for the Nelder-Mead algorithm if the user wishes to override the default (default: NULL). Initial values must be specified as a two-dimensional vector where both entries are between -0.5 and 0.5.

Value

groupsize: the number of patients required per arm, per stage
futility: the futility boundaries for the design
efficacy: the efficacy boundaries for the design
ess: the expected sample size at the delta0; the expected sample size at the delta1; and the maximum expected sample size
typeIerror: the actual type-I error rate of the design
power: the actual power of the design

Details

optgs uses the extended power-family of group-sequential tests, and searches for the values of the futility and efficacy shape parameters that optimise the specified weighting. A description of the extended power-family and optgs is provided in Wason (2012). The `weights' argument corresponds to the weight put on: 1) the expected sample size at delta=delta0; 2) the expected sample size at delta=delta1; 3) the maximum expected sample size; 4) the maximum sample size (i.e. J*groupsize).

References

Wason, J.M.S. OptGS: an R package for finding near-optimal group-sequential designs. Journal of Statistical Software, 66(2), 1-13. http://www.jstatsoft.org/v66/i02/

Examples

Run this code

threestagedeltaminimax=optgs(J=3,weights=c(0,0,1,0)) 
plot(threestagedeltaminimax)

Run the code above in your browser using DataLab