GenData: Generate longitudnal (full) data
In paulowhite/DelayedGSD: Planning and analysis of group sequential trial with pipeline data

GenData

R Documentation

Generate longitudnal (full) data

Description

Usage

GenData(
  n = 52,
  N.fw = 2,
  rand.block = c(1, 1, 0, 0),
  allsd = rep(3, N.fw + 1),
  mean0 = rep(0, N.fw + 1),
  delta = rep(0, N.fw + 1),
  ar = 0.86 * 2,
  cor.01.1 = -0.15,
  cor.ij.1 = 0.68,
  cor.0j.1 = -0.27,
  seed = 24082020,
  MissProb = NULL,
  DigitsOutcome = NULL,
  TimeFactor = 1,
  DigitsTime = NULL
)

Arguments

`n`	sample size
`N.fw`	number of follow-up measurements (equally spaced, after baseline)
`rand.block`	for block randomization
`allsd`	vector of sd of primary outcome at baseline and end of follow-up (main outcome)
`mean0`	mean outcome at each visit in control group
`delta`	treatment effect (i.e. difference in mean) on primary outcome at each visit
`ar`	accrual rate (average, unit is per time between the equally spaced visits)
`cor.01.1`	correlation between outcome at baseline and at first visit (main outcome)
`cor.ij.1`	correlation between outcome at two consecutive follow-up measurements (main outcome)
`cor.0j.1`	correlation between outcome at baseline and at any visit after the first visit (main outcome)
`seed`	integer for the random seed generator state.
`MissProb`	Missingness probability. Should be an N.fw-dimensional array with in each dimension the proportion of missing and non missing (in that order). For instance for two follow-up, should be a matrix containing the probability of missing both (1,1), missing only the first timepoint (1,2) missing only the second timepoint (2,1) or have complete data (2,2).
`DigitsOutcome`	Number of digits to round the outcome values (NULL means no rounding)
`TimeFactor`	Multiply the times by a factor (e.g. 14 if time between two follow-up visit should be approx 14 days)
`DigitsTime`	Number of digits to round the times (NULL means no rounding)