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Generate a dataset from public inputs only

Source: R/synpmx.R
synpmx_prior.Rd

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,
  roles = pmx_generated_roles(),
  n_subjects = NULL,
  seed = NULL,
  dropout = 0,
  lloq = NULL,
  covariates = NULL
)

Arguments

model

A pmx_structural_model().

design

A pmx_trial_design().

roles

A pmx_roles() naming the columns of the output. Defaults to pmx_generated_roles(), the package's own generated schema.

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 event-table form, under the names in 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.

Roles here name the output, not an input

Every generator in the package takes a pmx_roles() declaration and writes its output under those column names, so that one declaration serves the generator, compare_pmx_distributions() and synpmx_scorecard(). For synpmx_avatar(), synpmx_pca() and synpmx_model() the declaration describes the study being read. This function reads nothing, so the same object does the opposite job here: it prescribes the schema to generate into. Pass the roles of the study the synthetic data has to stand in for and the output drops straight into code written against it. A role left undeclared gets no column. The default, pmx_generated_roles(), names every column the generator can produce.

See also

synpmx_avatar(), synpmx_calibrated(), synpmx_empirical()

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

# Generating into a study's own schema, so the output needs no renaming.
study_roles <- pmx_roles(
  id = "SUBJID", time = "TIME", nominal_time = "NTIME", dv = "DV",
  amt = "AMT", evid = "EVID", cmt = "CMT", mdv = "MDV"
)
names(synpmx_prior(model, design, study_roles, n_subjects = 3, seed = 202))
#> [1] "SUBJID" "TIME"   "NTIME"  "DV"     "AMT"    "EVID"   "CMT"    "MDV"