harcode: Derive an SDTM variable with a hardcoded value

In sdtm.oak: SDTM Data Transformation Engine

Derive an SDTM variable with a hardcoded value

Description

• hardcode_no_ct() maps a hardcoded value to a target SDTM variable that has no terminology restrictions.

• hardcode_ct() maps a hardcoded value to a target SDTM variable with controlled terminology recoding.

Usage

hardcode_no_ct(
  tgt_dat = NULL,
  tgt_val,
  raw_dat,
  raw_var,
  tgt_var,
  id_vars = oak_id_vars()
)

hardcode_ct(
  tgt_dat = NULL,
  tgt_val,
  raw_dat,
  raw_var,
  tgt_var,
  ct_spec,
  ct_clst,
  id_vars = oak_id_vars()
)

Arguments

tgt_dat	Target dataset: a data frame to be merged against raw_dat by the variables indicated in id_vars. This parameter is optional, see section Value for how the output changes depending on this argument value.
tgt_val	The target SDTM value to be hardcoded into the variable indicated in tgt_var.
raw_dat	The raw dataset (dataframe); must include the variables passed in id_vars and raw_var.
raw_var	The raw variable: a single string indicating the name of the raw variable in raw_dat.
tgt_var	The target SDTM variable: a single string indicating the name of variable to be derived.
id_vars	Key variables to be used in the join between the raw dataset (raw_dat) and the target data set (raw_dat).
ct_spec	Study controlled terminology specification: a dataframe with a minimal set of columns, see ct_spec_vars() for details. This parameter is optional, if left as NULL no controlled terminology recoding is applied.
ct_clst	A codelist code indicating which subset of the controlled terminology to apply in the derivation. This parameter is optional, if left as NULL, all possible recodings in ct_spec are attempted.

Value

The returned data set depends on the value of tgt_dat:

• If no target dataset is supplied, meaning that tgt_dat defaults to NULL, then the returned data set is raw_dat, selected for the variables indicated in id_vars, and a new extra column: the derived variable, as indicated in tgt_var.

• If the target dataset is provided, then it is merged with the raw data set raw_dat by the variables indicated in id_vars, with a new column: the derived variable, as indicated in tgt_var.

Examples

md1 <-
  tibble::tribble(
    ~oak_id, ~raw_source, ~patient_number, ~MDRAW,
    1L,      "MD1",       101L,            "BABY ASPIRIN",
    2L,      "MD1",       102L,            "CORTISPORIN",
    3L,      "MD1",       103L,            NA_character_,
    4L,      "MD1",       104L,            "DIPHENHYDRAMINE HCL"
  )

# Derive a new variable `CMCAT` by overwriting `MDRAW` with the
# hardcoded value "GENERAL CONCOMITANT MEDICATIONS".
hardcode_no_ct(
  tgt_val = "GENERAL CONCOMITANT MEDICATIONS",
  raw_dat = md1,
  raw_var = "MDRAW",
  tgt_var = "CMCAT"
)

cm_inter <-
  tibble::tribble(
    ~oak_id, ~raw_source, ~patient_number, ~CMTRT,                ~CMINDC,
    1L,      "MD1",       101L,            "BABY ASPIRIN",        NA,
    2L,      "MD1",       102L,            "CORTISPORIN",         "NAUSEA",
    3L,      "MD1",       103L,            "ASPIRIN",             "ANEMIA",
    4L,      "MD1",       104L,            "DIPHENHYDRAMINE HCL", "NAUSEA",
    5L,      "MD1",       105L,            "PARACETAMOL",         "PYREXIA"
  )

# Derive a new variable `CMCAT` by overwriting `MDRAW` with the
# hardcoded value "GENERAL CONCOMITANT MEDICATIONS" with a prior join to
# `target_dataset`.
hardcode_no_ct(
  tgt_dat = cm_inter,
  tgt_val = "GENERAL CONCOMITANT MEDICATIONS",
  raw_dat = md1,
  raw_var = "MDRAW",
  tgt_var = "CMCAT"
)

# Controlled terminology specification
(ct_spec <- read_ct_spec_example("ct-01-cm"))

# Hardcoding of `CMCAT` with the value `"GENERAL CONCOMITANT MEDICATIONS"`
# involving terminology recoding. `NA` values in `MDRAW` are preserved in
# `CMCAT`.
hardcode_ct(
  tgt_dat = cm_inter,
  tgt_var = "CMCAT",
  raw_dat = md1,
  raw_var = "MDRAW",
  tgt_val = "GENERAL CONCOMITANT MEDICATIONS",
  ct_spec = ct_spec,
  ct_clst = "C66729"
)


# Variables are derived in sequence from multiple input sources.
# For each target variable, only missing (`NA`) values are filled
# during each step—previously assigned (non-missing) values are retained.

cm_raw <-
  tibble::tibble(
    oak_id = 1:4,
    raw_source = "cm_raw",
    patient_number = 370 + oak_id,
    PATNUM = patient_number,
    IT.CMTRT = c("BABY ASPIRIN", "CORTISPORIN", NA, NA),
    IT.CMTRTOTH = c("Other Treatment - ", NA, "Other Treatment - Baby Aspirin", NA)
  )

cm_raw

# Hardcoding of values of `CMCAT` is based firstly on the presence of missing
# values (`NA`) in `IT.CMTRT` and only secondly on `IT.CMTRTOTH`.
hardcode_no_ct(
  tgt_val = "General Concomitant Medications",
  raw_dat = cm_raw,
  raw_var = "IT.CMTRT",
  tgt_var = "CMCAT"
) |>
  hardcode_no_ct(
    tgt_val = "Other General Concomitant Medications",
    raw_dat = cm_raw,
    raw_var = "IT.CMTRTOTH",
    tgt_var = "CMCAT"
  )

# Note that hardcoding application is reversed in this example, this impacts
# the result.
hardcode_no_ct(
  tgt_val = "Other General Concomitant Medications",
  raw_dat = cm_raw,
  raw_var = "IT.CMTRTOTH",
  tgt_var = "CMCAT"
) |>
  hardcode_no_ct(
    tgt_val = "General Concomitant Medications",
    raw_dat = cm_raw,
    raw_var = "IT.CMTRT",
    tgt_var = "CMCAT"
  )

sdtm.oak documentation built on June 9, 2025, 5:10 p.m.