| Type: | Package |
| Title: | Near-Optimal Group-Sequential Designs for Continuous Outcomes |
| Version: | 1.2 |
| Date: | 2024-02-27 |
| Maintainer: | James Wason <james.wason@newcastle.ac.uk> |
| Description: | Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. 'OptGS' provides functions to quickly search for near-optimal group-sequential designs for normally distributed outcomes. The methods used are described in Wason, JMS (2015) <doi:10.18637/jss.v066.i02>. |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| Packaged: | 2024-02-28 13:18:08 UTC; njw228 |
| Author: | James Wason [aut, cre], John Burkardt [ctb], R O'Neill [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2024-02-29 13:02:48 UTC |
Different generic functions for class OptGS
Description
Generic functions for summarising an object of class OptGS
Usage
## S3 method for class 'OptGS'
print(x,... )
## S3 method for class 'OptGS'
plot(x,ylim=NULL,...)
Arguments
x |
An output object of class OptGS |
ylim |
y limits to be passed to plot |
... |
Additional arguments to be passed. |
Details
print.OptGS gives the group-size, stopping boundaries, and operating characteristics of the design
plot.OptGS produces a plot of the expected sample size as the standardised treatment effect differs
Value
Screen or graphics output.
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) |
J |
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. |
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).
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 |
References
Wason, J.M.S. OptGS: an R package for finding near-optimal group-sequential designs. Journal of Statistical Software, 66(2), 1-13. https://www.jstatsoft.org/v66/i02/
Examples
##Find a three-stage design that minimises the maximum expected sample size.
threestagedeltaminimax=optgs(J=3,weights=c(0,0,1,0))
plot(threestagedeltaminimax)
Finding extended power-family group-sequential designs
Description
powerfamily is used to find a one-sided extended power-family group-sequential design
Usage
powerfamily(futility = 0, efficacy = 0, delta0 = 0, delta1 = 1/3,
J = 2, sigma = 1, sd.known = TRUE, alpha = 0.05, power = 0.9)
Arguments
futility |
shape parameter for futility boundaries (default: 0) |
efficacy |
shape parameter for efficacy boundaries (default: 0) |
delta0 |
mean difference in treatment effect under the null hypothesis (default: 0) |
delta1 |
clinically relevant difference used to power the trial (default: 1/3) |
J |
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) |
Details
powerfamily uses the extended power-family of group-sequential tests. A description of the extended power-family is provided in Wason (2012).
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 |
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
##Find a three-stage design that has shape parameters -0.5 and 0.5.
threestagedesign=powerfamily(J=3,futility=-0.5,efficacy=0.5)
plot(threestagedesign)