simulate_test_data function

Create simulated datasets

Creates a longitudinal dataset in the format that rbmi was designed to analyse.

simulate_test_data(
  n = 200,
  sd = c(3, 5, 7),
  cor = c(0.1, 0.7, 0.4),
  mu = list(int = 10, age = 3, sex = 2, trt = c(0, 4, 8), visit = c(0, 1, 2))
)

as_vcov(sd, cor)

Arguments

• n: the number of subjects to sample. Total number of observations returned is thus n * length(sd)
• sd: the standard deviations for the outcome at each visit. i.e. the square root of the diagonal of the covariance matrix for the outcome
• cor: the correlation coefficients between the outcome values at each visit. See details.
• mu: the coefficients to use to construct the mean outcome value at each visit. Must be a named list with elements int, age, sex, trt & visit. See details.

Details

The number of visits is determined by the size of the variance covariance matrix. i.e. if 3 standard deviation values are provided then 3 visits per patient will be created.

The covariates in the simulated dataset are produced as follows:

• Patients age is sampled at random from a N(0,1) distribution
• Patients sex is sampled at random with a 50/50 split
• Patients group is sampled at random but fixed so that each group has n/2 patients
• The outcome variable is sampled from a multivariate normal distribution, see below for details

The mean for the outcome variable is derived as:

outcome = Intercept + age + sex + visit + treatment

The coefficients for the intercept, age and sex are taken from mu$int, mu$age and mu$sexrespectively, all of which must be a length 1 numeric.

Treatment and visit coefficients are taken from mu$trt and mu$visit respectively and must either be of length 1 (i.e. a constant affect across all visits) or equal to the number of visits (as determined by the length of sd). I.e. if you wanted a treatment slope of 5 and a visit slope of 1 you could specify:

mu = list(..., "trt" = c(0,5,10), "visit" = c(0,1,2))

The correlation matrix is constructed from cor as follows. Let cor = c(a, b, c, d, e, f) then the correlation matrix would be:

1  a  b  d
a  1  c  e
b  c  1  f
d  e  f  1