Reformat NCA-type analysis-ready dataset (ARD) into a NONMEM-style modeling dataset

Usage

reformat_data_nca_to_modeling(
  data,
  dictionary = list(subject_id = "ID", group = "GROUP", time = "TIME", dose = "AMT", conc
    = "DV"),
  dose_compartment = 1,
  obs_compartment = 1,
  covariates = NULL,
  repeat_doses = NULL,
  categorical_mapping = NULL,
  na = "."
)

Arguments

data: dataset formatted as NCA analysis-ready dataset
dictionary: a data dictionary that maps expected variable names to variables in the data.
dose_compartment: the compartment in which doses are entered
obs_compartment: the observation compartment number
covariates: a vector of covariate names that are to be extracted and added to the modeling dataset.
repeat_doses: Optional list for repeated dosing (MAD studies). Must contain interval (dosing interval in TIME units). Optionally contains n (total number of doses). If n is omitted, it is inferred per subject/group as ceiling(max(observation_time) / interval). Only applies to column-wise dose data. Default NULL preserves existing behavior (no ADDL/II columns). Examples: list(interval = 12) or list(n = 5, interval = 12).
categorical_mapping: Either a character vector of column names to auto-encode (most common value gets 0, next gets 1, etc.), or a data.frame with columns column, original_value, encoded_value for explicit mappings. NA values are encoded as -99. The final mapping is attached as a "categorical_mapping" attribute on the returned data.frame. Default NULL skips explicit encoding (existing blanket conversion still applies).
na: what to set NA values to. E.g. ".", (default) or NA (keep NA), or NULL (do nothing).

Value

data.frame with population PK input data in NONMEM-style format.