GenData | R Documentation |
yy
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
)
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) |
zz
ff
Paul Blanche
x <- GenData()
head(x$d,n=20)
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