Generate a dataset from public inputs only

Simulates from a public structural model and a public protocol. No confidential data is read, so there is nothing to protect and no budget to spend: this is epsilon = 0, the strongest possible guarantee.

Usage

synpmx_prior(
  model,
  design,
  n_subjects = NULL,
  seed = NULL,
  dropout = 0,
  lloq = NULL,
  covariates = NULL
)

Arguments

model: A pmx_structural_model().
design: A pmx_trial_design().
n_subjects: Number of subjects. Defaults to the planned cohort total.
seed: Ordinary generation seed. Unrelated to privacy noise.
dropout: Fraction of subjects who discontinue early. A public assumption from the protocol.
lloq: Lower limit of quantification. Observations below it are flagged CENS = 1 with DV at the limit, following the Monolix convention.
covariates: Optional pmx_covariates().

Value

A data frame in the generated event-table schema; see pmx_generated_roles().

Details

The typical parameter values must come from somewhere that is not the data – allometric scaling from preclinical work, a published model for the compound class, or the reasoning that set the starting dose. The output is exactly as good as that prior.

Examples

model <- pmx_structural_model(
  pk = "1cmt_oral", typical = c(cl = 6, v = 35, ka = 1.5),
  source = "illustrative allometric scaling"
)
design <- pmx_trial_design(
  dose_levels = 320, cohort_sizes = 12, sampling = c(0, 1, 2, 4, 9, 24),
  source = "illustrative protocol"
)
syn <- synpmx_prior(model, design, n_subjects = 12, seed = 202)
head(syn, 3)
#>   ID      TIME NTIME       TAD OCC       DV AMT RATE EVID CMT DVID MDV CENS
#> 1  1 0.0000000     0 0.0000000   1       NA 320    0    1   1 <NA>   1    0
#> 2  1 0.0000000     0 0.0000000   1 0.000000   0    0    0   2   cp   0    0
#> 3  1 0.9791034     1 0.9791034   1 6.823022   0    0    0   2   cp   0    0
#>   DOSE
#> 1  320
#> 2  320
#> 3  320