Reformat SDTM datasets into NONMEM-style modeling dataset

Usage

reformat_data_sdtm_to_modeling(
  data,
  dictionary,
  categorical_mapping = NULL,
  na = "."
)

Arguments

data: list containing data.frames with SDTM domains
dictionary: a data dictionary that maps expected variable names to variables in the data.
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 encoding.
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. It will also add the non-standard columns ROUTE ("oral", "iv") and FORM (formulation: "tablet", "suspension", "patch", "infusion", etc.) with values for each dose and NA for observations.