spass (version 1.2)

bssr.gee.1subgroup: Blinded Sample Size Recalculation for longitudinal data in a One Subgroup Design

Description

Given re-estimations from an Internal Pilot Study (IPS), bssr.GEE.1subgroup re-estimates required sample size given the re-estimated nuisance parameters are given. bssr.gee.1subgroup is a wrapper for n.gee.1subgroup where the re-estimation of the variances can be highly dependable on the user and should be supplied separately. see "detail" for more information.

Usage

bssr.gee.1subgroup(alpha, tail = "both", beta = NULL, delta, estsigma,
  tau = 0.5, k = 1)

Arguments

alpha

level (type I error) to which the hypothesis is tested.

tail

which type of test is used, e.g. which quartile und H0 is calculated.

beta

type II error (power=1-beta) to which an alternative should be proven.

delta

vector of estimated treatment effect in overall and sub population, c(overall population, only subpopulation).

estsigma

vector of re-estimated standard deviations, c(full population, subpopulation). See 'Details'.

tau

ratio between complementary F/S and sub-population S.

k

treatment allocation factor between groups: see 'Details'.

Value

bssr.gee.1subgroup returns a list containing the recalculated sample sizes along with all relevant parameters. Use summary.bssrest for a structured overview.

Details

This function provides a simple warped for n.gee.1subgroup where instead of initial assumptions, reestimated nuisance parameter are used. For more information see n.gee.1subgroup. Required samplesize to test alternative delta with specified power 1-beta when testing the global null hypothesis \(H_0: \beta_3^F=\beta_3^S=0\) to level alpha is estimated. When testing outcomes have variance estsigma.

For sample sizes \(n_C\) and \(n_T\) of the control and treatment group respectively, the argument k is the sample size allocation factor, i.e. \(k = n_T/n_C\) and tau represents the ratio of the sub-population.

See Also

n.gee.1subgroup for sample size calculation prior to a trial and estimcov how the re-estimate nuisance parameters. See \(sim.gee\) for a working example for an initial sample size estimation and a re-estimation mid trial.

Examples

# NOT RUN {
estimate<-bssr.gee.1subgroup(alpha=0.05,beta=0.2,delta=c(0.1,0.1),estsigma=c(0.8,0.4),tau=0.4, k=1)
summary(estimate)
# }