findSimonDesigns: Find Simon designs

In martinlaw/SCsinglearm: Finds Stochastically Curtailed Design Realisations for Single-arm Binary Outcome Trials

Description

This function finds Simon designs for a given set of design parameters. It returns not only the optimal and minimax design realisations, but all design realisations that could be considered "best" in terms of expected sample size under p=p0 (EssH0), expected sample size under p=p1 (Ess), maximum sample size (n) or any weighted combination of these three optimality criteria.

Usage

findSimonDesigns(nmin, nmax, p0, p1, alpha, power, benefit = FALSE)

Arguments

nmin	Minimum permitted sample size. Should be a multiple of block size or number of stages.
nmax	Maximum permitted sample size. Should be a multiple of block size or number of stages.
p0	Probability for which to control the type-I error-rate
p1	Probability for which to control the power
alpha	Significance level
power	Required power (1-beta)
benefit	Allow the trial to end for a go decision and reject the null hypothesis at the interim analysis (i.e., the design of Mander and Thompson)

Value

A list of class "SCsinglearm_simon" containing two data frames. The first data frame, $input, has a single row and contains all the inputted values. The second data frame, $all.des, contains one row for each design realisation, and contains the details of each design, including sample size, stopping boundaries and operating characteristics. To see a diagram of any obtained design realisation, simply call the function drawDiagram with this output as the only argument.

Author(s)

Martin Law, [e-mail]

References

A.P. Mander, S.G. Thompson, Two-stage designs optimal under the alternative hypothesis for phase II cancer clinical trials, Contemporary Clinical Trials, Volume 31, Issue 6, 2010, Pages 572-578

Richard Simon, Optimal two-stage designs for phase II clinical trials, Controlled Clinical Trials, Volume 10, Issue 1, 1989, Pages 1-10

martinlaw/SCsinglearm documentation built on Feb. 19, 2021, 8:05 p.m.