sim.bssr.gee.1subgroup: Simulation of a longitudinal one subgroup design with...
In spass: Study Planning and Adaptation of Sample Size

Description Usage Arguments Details Value Source See Also Examples

Given estimates of the treatment effects to be proven, the variances, and the prevalence, sim.bssr.gee.1subgroup calculates an initial sample size and performs a blinded sample size recalculation after a pre-specified number of subjects have been enrolled. Each observation is simulated and a final analysis executed. Several variations are included, such as different approximations or sample size allocation.

sim.bssr.gee.1subgroup(
  nsim = 1000,
  alpha = 0.05,
  tail = "both",
  beta = 0.2,
  delta = c(0.1, 0.1),
  vdelta = c(0.1, 0.1),
  sigma_pop = c(3, 3),
  vsigma_pop = c(3, 3),
  tau = 0.5,
  rho = 0.25,
  vrho = 0.25,
  theta = 1,
  vtheta = 1,
  Time = 0:5,
  rec.at = 0.5,
  k = 1,
  model = 1,
  V = diag(rep(1, length(Time))),
  OD = 0,
  vdropout = rep(0, length(Time)),
  missingtype = "none",
  vmissingtype = "none",
  seed = 2015
)

`nsim`	number of simulation runs.
`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 true treatment effects, c(overall population, inside subgroup).
`vdelta`	vector of treatment effects to be proven, c(overall population, inside subgroup).
`sigma_pop`	vector of true standard deviations of the treatment effects, c(overall population, subgroup).
`vsigma_pop`	vector of assumed standard deviations, c(overall population, inside subgroup).
`tau`	subgroup prevalence.
`rho`	true correlation coefficient between two adjacent timepoints
`vrho`	initial expectation of the correlation coefficient between two adjacent timepoints
`theta`	true correlation absorption coefficient if timepoints are farther apart
`vtheta`	expected correlation absorption coefficient if timepoints are farther apart
`Time`	vector of measured timepoints
`rec.at`	blinded sample size review is performed after `rec.at`*100\% subjects of the initial sample size calculation.
`k`	sample size allocation factor between groups: see 'Details'.
`model`	which of the two often revered statistical models should be used?: see 'Details'.
`V`	working covariance matrix.
`OD`	overall dropout measured at last timepoint
`vdropout`	vector of expected dropouts per timepoint if missingness is to be expected
`missingtype`	true missingtype underlying the missingness
`vmissingtype`	initial assumptions about the missingtype underlying the missingness
`seed`	set seed value for the simulations to compare results.

This function combines sample size estimation, blinded sample size re-estimation and analysis in a design with a subgroup within a full population where we want to test for treatment effects between a control and a treatment group. The required sample size for the control and treatment group to prove an existing alternative delta with a specified power 1-beta when testing the global null hypothesis H_0: Δ_F=Δ_S=0 to level alpha is calculated prior to the study and then recalculated in an internal pilot study.

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.

sim.bssr.1subgroup returns a data.frame containing the mean and variance of recalculated sample sizes within the control group and treatment group respectively and the achieved simulated power along with all relevant parameters.

sim.bssr.gee.1subgroup uses code contributed by Roland Gerard Gera.

sim.bssr.gee.1subgroup makes use of n.gee.1subgroup, bssr.gee.1subgroup, and r.gee.1subgroup.