VS

Introduction

This article describes how to create a Findings SDTM domain using the {sdtm.oak} package. Examples are currently presented and tested in the context of the VS domain.

Before reading this article, it is recommended that users review some of the articles in the package documentation of {sdtm.oak} to understand some of the key concepts: Algorithms & Sub-Algorithms, Creating an Interventions Domain, which provides a detailed explanation of various concepts in {sdtm.oak}, such as oak_id_vars, condition_add, etc. It also offers guidance on which mapping algorithms or functions to use for different mappings and provides a more detailed explanation of how these mapping algorithms or functions work.

In this article, we will dive directly into programming and provide further explanation only where it is required.

Programming workflow

In {sdtm.oak} we process one raw dataset at a time. Similar raw datasets (example Vital Signs - Screening (vs_raw), Vital Signs - Treatment (vs_t_raw)) can be stacked together before processing or can be processed separately.

Read in data
Create oak_id_vars
Read in CT
Map Topic Variable
Map Rest of the Variables
Repeat Map Topic and Map Rest

Repeat the above steps for different raw datasets before proceeding with the below steps.

Create SDTM derived variables
Add Labels and Attributes

Read in data

Read all the raw datasets into the environment. In this example, the raw dataset name is vs_raw. Users can read it from the package using the below code:

library(sdtm.oak)
library(pharmaverseraw)
library(dplyr)

# Read in input data
vs_raw <- pharmaverseraw::vs_raw
dm <- pharmaversesdtm::dm

Vital Signs Raw dataset

Sample of Data

SDTM aCRF

SDTM annotated CRF for the vs_raw can be viewed here:

Create oak_id_vars

vs_raw <- vs_raw %>%
  generate_oak_id_vars(
    pat_var = "PATNUM",
    raw_src = "vitals"
  )

Read in CT

Controlled Terminology is part of the SDTM specification and it is prepared by the user. In this example, the study controlled terminology name is sdtm_ct.csv. Users can read it from the package using the below code:

study_ct <- read.csv("metadata/sdtm_ct.csv")

Sample of Data

Map Topic Variable

This raw dataset has multiple topic variables. Lets start with SYSBP. Map topic variable SYSBP from the raw variable SYS_BP.

# Map topic variable SYSBP
vs_sysbp <-
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "SYS_BP",
    tgt_var = "VSTESTCD",
    tgt_val = "SYSBP",
    ct_spec = study_ct,
    ct_clst = "C66741"
  ) %>%
  # Filter for records where VSTESTCD is not empty.
  # Only these records need qualifier mappings.
  dplyr::filter(!is.na(.data$VSTESTCD))

Sample of Data

Map Rest of the Variables

Map rest of the variables applicable to the topic variable SYSBP. This can include qualifiers, identifier and timing variables.

## Map Rest of the Variables
vs_sysbp <- vs_sysbp %>%
  # Map VSTEST using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "SYS_BP",
    tgt_var = "VSTEST",
    tgt_val = "Systolic Blood Pressure",
    ct_spec = study_ct,
    ct_clst = "C67153",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRES using assign_no_ct algorithm
  assign_no_ct(
    raw_dat = vs_raw,
    raw_var = "SYS_BP",
    tgt_var = "VSORRES",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "SYS_BP",
    tgt_var = "VSORRESU",
    tgt_val = "mmHg",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSPOS using assign_ct algorithm
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "SUBPOS",
    tgt_var = "VSPOS",
    ct_spec = study_ct,
    ct_clst = "C71148",
    id_vars = oak_id_vars()
  )

Sample of Data

Repeat Map Topic and Map Rest

This raw data source has other topic variables DIABP, PULSE, HEIGHT, WEIGHT, TEMP and its corresponding qualifiers. Repeat mapping topic and qualifiers for each topic variable.

# Repeat Map Topic and Map Rest
vs_diabp <-
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "DIA_BP",
    tgt_var = "VSTESTCD",
    tgt_val = "DIABP",
    ct_spec = study_ct,
    ct_clst = "C66741"
  ) %>%
  dplyr::filter(!is.na(.data$VSTESTCD)) %>%
  # Map VSTEST using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "DIA_BP",
    tgt_var = "VSTEST",
    tgt_val = "Diastolic Blood Pressure",
    ct_spec = study_ct,
    ct_clst = "C67153",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRES using assign_no_ct algorithm
  assign_no_ct(
    raw_dat = vs_raw,
    raw_var = "DIA_BP",
    tgt_var = "VSORRES",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "DIA_BP",
    tgt_var = "VSORRESU",
    tgt_val = "mmHg",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSPOS using assign_ct algorithm
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "SUBPOS",
    tgt_var = "VSPOS",
    ct_spec = study_ct,
    ct_clst = "C71148",
    id_vars = oak_id_vars()
  )

# Map topic variable PULSE and its qualifiers.
vs_pulse <-
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "PULSE",
    tgt_var = "VSTESTCD",
    tgt_val = "PULSE",
    ct_spec = study_ct,
    ct_clst = "C66741"
  ) %>%
  dplyr::filter(!is.na(.data$VSTESTCD)) %>%
  # Map VSTEST using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "PULSE",
    tgt_var = "VSTEST",
    tgt_val = "Pulse Rate",
    ct_spec = study_ct,
    ct_clst = "C67153",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRES using assign_no_ct algorithm
  assign_no_ct(
    raw_dat = vs_raw,
    raw_var = "PULSE",
    tgt_var = "VSORRES",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "PULSE",
    tgt_var = "VSORRESU",
    tgt_val = "beats/min",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSPOS using assign_ct algorithm
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "SUBPOS",
    tgt_var = "VSPOS",
    ct_spec = study_ct,
    ct_clst = "C71148",
    id_vars = oak_id_vars()
  )

# Map topic variable TEMP from raw variable TEMP and its qualifiers.
vs_temp <-
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.TEMP",
    tgt_var = "VSTESTCD",
    tgt_val = "TEMP",
    ct_spec = study_ct,
    ct_clst = "C66741"
  ) %>%
  dplyr::filter(!is.na(.data$VSTESTCD)) %>%
  # Map VSTEST using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.TEMP",
    tgt_var = "VSTEST",
    tgt_val = "Temperature",
    ct_spec = study_ct,
    ct_clst = "C67153",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRES using assign_no_ct algorithm
  assign_no_ct(
    raw_dat = vs_raw,
    raw_var = "IT.TEMP",
    tgt_var = "VSORRES",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.TEMP",
    tgt_var = "VSORRESU",
    tgt_val = "F",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSLOC from TEMPLOC using assign_ct
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "IT.TEMP_LOC",
    tgt_var = "VSLOC",
    ct_spec = study_ct,
    ct_clst = "C74456",
    id_vars = oak_id_vars()
  ) %>%
  # Create VSSTRESC by converting VSORRES from F to C
  mutate(VSSTRESC = as.character(sprintf("%.2f", (as.numeric(VSORRES) - 32) * 5 / 9))) %>%
  # Map VSSTRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.TEMP",
    tgt_var = "VSSTRESU",
    tgt_val = "C",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  )

# Map topic variable HEIGHT from raw variable IT.HEIGHT_VSORRRES and its qualifiers.
vs_height <-
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.HEIGHT_VSORRES",
    tgt_var = "VSTESTCD",
    tgt_val = "HEIGHT",
    ct_spec = study_ct,
    ct_clst = "C66741"
  ) %>%
  dplyr::filter(!is.na(.data$VSTESTCD)) %>%
  # Map VSTEST using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.HEIGHT_VSORRES",
    tgt_var = "VSTEST",
    tgt_val = "Height",
    ct_spec = study_ct,
    ct_clst = "C67153",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRES using assign_no_ct algorithm
  assign_no_ct(
    raw_dat = vs_raw,
    raw_var = "IT.HEIGHT_VSORRES",
    tgt_var = "VSORRES",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.HEIGHT_VSORRES",
    tgt_var = "VSORRESU",
    tgt_val = "in",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  ) %>%
  # Create VSSRESC by converting VSORRES from in to cm
  mutate(VSSTRESC = as.character(sprintf("%.2f", as.numeric(VSORRES) * 2.54))) %>%
  # Map VSSTRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.HEIGHT_VSORRES",
    tgt_var = "VSSTRESU",
    tgt_val = "cm",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  )

# Map topic variable WEIGHT from raw variable IT.WEIGHT and its qualifiers.
vs_weight <-
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.WEIGHT",
    tgt_var = "VSTESTCD",
    tgt_val = "WEIGHT",
    ct_spec = study_ct,
    ct_clst = "C66741"
  ) %>%
  dplyr::filter(!is.na(.data$VSTESTCD)) %>%
  # Map VSTEST using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.WEIGHT",
    tgt_var = "VSTEST",
    tgt_val = "Weight",
    ct_spec = study_ct,
    ct_clst = "C67153",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRES using assign_no_ct algorithm
  assign_no_ct(
    raw_dat = vs_raw,
    raw_var = "IT.WEIGHT",
    tgt_var = "VSORRES",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.WEIGHT",
    tgt_var = "VSORRESU",
    tgt_val = "LB",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  ) %>%
  # Create VSSTRESC by converting VSORRES from LB to KG
  mutate(VSSTRESC = as.character(sprintf("%.2f", as.numeric(VSORRES) / 2.20462))) %>%
  # Map VSSTRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "IT.WEIGHT",
    tgt_var = "VSSTRESU",
    tgt_val = "kg",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  )

Now that all the topic variable and its qualifier mappings are complete, combine all the datasets and proceed with mapping qualifiers, identifiers and timing variables applicable to all topic variables.

# Combine all the topic variables into a single data frame.
vs_combined <- dplyr::bind_rows(
  vs_diabp, vs_height, vs_pulse,
  vs_sysbp, vs_temp, vs_weight
)

# Map qualifiers common to all topic variables
vs <- vs_combined %>%
  # Map VSDTC using assign_ct algorithm
  assign_datetime(
    raw_dat = vs_raw,
    raw_var = c("VTLD"),
    tgt_var = "VSDTC",
    raw_fmt = c(list(c("d-m-y", "dd-mmm-yyyy")))
  ) %>%
  # Map VSTPT from TMPTC using assign_ct
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "TMPTC",
    tgt_var = "VSTPT",
    ct_spec = study_ct,
    ct_clst = "TPT",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSTPTNUM from TMPTC using assign_ct
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "TMPTC",
    tgt_var = "VSTPTNUM",
    ct_spec = study_ct,
    ct_clst = "TPTNUM",
    id_vars = oak_id_vars()
  ) %>%
  # Map VISIT from INSTANCE using assign_ct
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "INSTANCE",
    tgt_var = "VISIT",
    ct_spec = study_ct,
    ct_clst = "VISIT",
    id_vars = oak_id_vars()
  ) %>%
  # Map VISITNUM from INSTANCE using assign_ct
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "INSTANCE",
    tgt_var = "VISITNUM",
    ct_spec = study_ct,
    ct_clst = "VISITNUM",
    id_vars = oak_id_vars()
  )

Sample of Data

Create SDTM derived variables

Create derived variables applicable to all topic variables.

vs <- vs %>%
  dplyr::mutate(
    STUDYID = "CDISCPILOT01",
    DOMAIN = "VS",
    VSCAT = "VITAL SIGNS",
    USUBJID = paste0("01", "-", .data$patient_number),
    VSSTRESC = ifelse(is.na(VSSTRESC), VSORRES, VSSTRESC),
    VSSTRESN = as.numeric(VSSTRESC),
    VSSTRESU = ifelse(is.na(VSSTRESU), VSORRESU, VSSTRESU),
    VSELTM = ifelse(is.na(VSTPT), NA, paste0("PT", readr::parse_number(VSTPT), "M")),
    VSTPTREF = ifelse(is.na(VSPOS), NA, paste("PATIENT", VSPOS))
  ) %>%
  arrange(USUBJID, VSTESTCD, as.numeric(VISITNUM), as.numeric(VSTPTNUM)) %>%
  derive_seq(
    tgt_var = "VSSEQ",
    rec_vars = c("USUBJID", "VSTESTCD")
  ) %>%
  derive_study_day(
    sdtm_in = .,
    dm_domain = dm,
    tgdt = "VSDTC",
    refdt = "RFXSTDTC",
    study_day_var = "VSDY"
  ) %>%
  dplyr::select("STUDYID", "DOMAIN", "USUBJID", "VSSEQ", "VSTESTCD", "VSTEST", "VSPOS", "VSORRES", "VSORRESU", "VSSTRESC", "VSSTRESN", "VSSTRESU", "VSLOC", "VISITNUM", "VISIT", "VSDTC", "VSDY", "VSTPT", "VSTPTNUM", "VSELTM", "VSTPTREF")

Sample of Data

Add Labels and Attributes

Yet to be developed. Please refer to {metatools} package to investigate options.

--- title: "VS" order: 2 --- ```{r setup script, include=FALSE, purl=FALSE} invisible_hook_purl <- function(before, options, ...) { knitr::hook_purl(before, options, ...) NULL } knitr::knit_hooks$set(purl = invisible_hook_purl) source("functions/print_df.R") ``` # Introduction This article describes how to create a Findings SDTM domain using the `{sdtm.oak}` package. Examples are currently presented and tested in the context of the `VS` domain. Before reading this article, it is recommended that users review some of the articles in the package documentation of `{sdtm.oak}` to understand some of the key concepts: [Algorithms & Sub-Algorithms](https://pharmaverse.github.io/sdtm.oak/articles/algorithms.html), [Creating an Interventions Domain](https://pharmaverse.github.io/sdtm.oak/articles/interventions_domain.html), which provides a detailed explanation of various concepts in `{sdtm.oak}`, such as `oak_id_vars`, `condition_add`, etc. It also offers guidance on which mapping algorithms or functions to use for different mappings and provides a more detailed explanation of how these mapping algorithms or functions work. In this article, we will dive directly into programming and provide further explanation only where it is required. # Programming workflow In `{sdtm.oak}` we process one raw dataset at a time. Similar raw datasets (example Vital Signs - Screening (`vs_raw`), Vital Signs - Treatment (`vs_t_raw`)) can be stacked together before processing or can be processed separately. - [Read in data](#readdata) - [Create oak_id_vars](#oakidvars) - [Read in CT](#readct) - [Map Topic Variable](#maptopic) - [Map Rest of the Variables](#maprest) - [Repeat Map Topic and Map Rest](#repeatsteps) Repeat the above steps for different raw datasets before proceeding with the below steps. - [Create SDTM derived variables](#derivedvars) - [Add Labels and Attributes](#attributes) ## Read in data {#readdata} Read all the raw datasets into the environment. In this example, the raw dataset name is `vs_raw`. Users can read it from the package using the below code: ```{r setup, message=FALSE, warning=FALSE, results='hold'} library(sdtm.oak) library(pharmaverseraw) library(dplyr) # Read in input data vs_raw <- pharmaverseraw::vs_raw dm <- pharmaversesdtm::dm ``` Vital Signs Raw dataset ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(vs_raw, n = 5) ``` ### SDTM aCRF SDTM annotated CRF for the `vs_raw` can be viewed [here](https://github.com/pharmaverse/pharmaverseraw/blob/main/vignettes/articles/aCRFs/VitalSigns_aCRF.pdf): ## Create oak_id_vars {#oakidvars} ```{r} vs_raw <- vs_raw %>% generate_oak_id_vars( pat_var = "PATNUM", raw_src = "vitals" ) ``` ## Read in CT {#readct} Controlled Terminology is part of the SDTM specification and it is prepared by the user. In this example, the study controlled terminology name is `sdtm_ct.csv`. Users can read it from the package using the below code: ```{r, echo = TRUE} study_ct <- read.csv("metadata/sdtm_ct.csv") ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(study_ct, n = 10) ``` ## Map Topic Variable {#maptopic} This raw dataset has multiple topic variables. Lets start with `SYSBP`. Map topic variable `SYSBP` from the raw variable `SYS_BP`. ```{r} # Map topic variable SYSBP vs_sysbp <- hardcode_ct( raw_dat = vs_raw, raw_var = "SYS_BP", tgt_var = "VSTESTCD", tgt_val = "SYSBP", ct_spec = study_ct, ct_clst = "C66741" ) %>% # Filter for records where VSTESTCD is not empty. # Only these records need qualifier mappings. dplyr::filter(!is.na(.data$VSTESTCD)) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(vs_sysbp, n = 10) ``` ## Map Rest of the Variables {#maprest} Map rest of the variables applicable to the topic variable `SYSBP`. This can include qualifiers, identifier and timing variables. ```{r} ## Map Rest of the Variables vs_sysbp <- vs_sysbp %>% # Map VSTEST using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "SYS_BP", tgt_var = "VSTEST", tgt_val = "Systolic Blood Pressure", ct_spec = study_ct, ct_clst = "C67153", id_vars = oak_id_vars() ) %>% # Map VSORRES using assign_no_ct algorithm assign_no_ct( raw_dat = vs_raw, raw_var = "SYS_BP", tgt_var = "VSORRES", id_vars = oak_id_vars() ) %>% # Map VSORRESU using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "SYS_BP", tgt_var = "VSORRESU", tgt_val = "mmHg", ct_spec = study_ct, ct_clst = "C66770", id_vars = oak_id_vars() ) %>% # Map VSPOS using assign_ct algorithm assign_ct( raw_dat = vs_raw, raw_var = "SUBPOS", tgt_var = "VSPOS", ct_spec = study_ct, ct_clst = "C71148", id_vars = oak_id_vars() ) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(vs_sysbp, n = 10) ``` ## Repeat Map Topic and Map Rest {#repeatsteps} This raw data source has other topic variables `DIABP`, `PULSE`, `HEIGHT`, `WEIGHT`, `TEMP` and its corresponding qualifiers. Repeat mapping topic and qualifiers for each topic variable. ```{r} # Repeat Map Topic and Map Rest vs_diabp <- hardcode_ct( raw_dat = vs_raw, raw_var = "DIA_BP", tgt_var = "VSTESTCD", tgt_val = "DIABP", ct_spec = study_ct, ct_clst = "C66741" ) %>% dplyr::filter(!is.na(.data$VSTESTCD)) %>% # Map VSTEST using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "DIA_BP", tgt_var = "VSTEST", tgt_val = "Diastolic Blood Pressure", ct_spec = study_ct, ct_clst = "C67153", id_vars = oak_id_vars() ) %>% # Map VSORRES using assign_no_ct algorithm assign_no_ct( raw_dat = vs_raw, raw_var = "DIA_BP", tgt_var = "VSORRES", id_vars = oak_id_vars() ) %>% # Map VSORRESU using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "DIA_BP", tgt_var = "VSORRESU", tgt_val = "mmHg", ct_spec = study_ct, ct_clst = "C66770", id_vars = oak_id_vars() ) %>% # Map VSPOS using assign_ct algorithm assign_ct( raw_dat = vs_raw, raw_var = "SUBPOS", tgt_var = "VSPOS", ct_spec = study_ct, ct_clst = "C71148", id_vars = oak_id_vars() ) # Map topic variable PULSE and its qualifiers. vs_pulse <- hardcode_ct( raw_dat = vs_raw, raw_var = "PULSE", tgt_var = "VSTESTCD", tgt_val = "PULSE", ct_spec = study_ct, ct_clst = "C66741" ) %>% dplyr::filter(!is.na(.data$VSTESTCD)) %>% # Map VSTEST using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "PULSE", tgt_var = "VSTEST", tgt_val = "Pulse Rate", ct_spec = study_ct, ct_clst = "C67153", id_vars = oak_id_vars() ) %>% # Map VSORRES using assign_no_ct algorithm assign_no_ct( raw_dat = vs_raw, raw_var = "PULSE", tgt_var = "VSORRES", id_vars = oak_id_vars() ) %>% # Map VSORRESU using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "PULSE", tgt_var = "VSORRESU", tgt_val = "beats/min", ct_spec = study_ct, ct_clst = "C66770", id_vars = oak_id_vars() ) %>% # Map VSPOS using assign_ct algorithm assign_ct( raw_dat = vs_raw, raw_var = "SUBPOS", tgt_var = "VSPOS", ct_spec = study_ct, ct_clst = "C71148", id_vars = oak_id_vars() ) # Map topic variable TEMP from raw variable TEMP and its qualifiers. vs_temp <- hardcode_ct( raw_dat = vs_raw, raw_var = "IT.TEMP", tgt_var = "VSTESTCD", tgt_val = "TEMP", ct_spec = study_ct, ct_clst = "C66741" ) %>% dplyr::filter(!is.na(.data$VSTESTCD)) %>% # Map VSTEST using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "IT.TEMP", tgt_var = "VSTEST", tgt_val = "Temperature", ct_spec = study_ct, ct_clst = "C67153", id_vars = oak_id_vars() ) %>% # Map VSORRES using assign_no_ct algorithm assign_no_ct( raw_dat = vs_raw, raw_var = "IT.TEMP", tgt_var = "VSORRES", id_vars = oak_id_vars() ) %>% # Map VSORRESU using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "IT.TEMP", tgt_var = "VSORRESU", tgt_val = "F", ct_spec = study_ct, ct_clst = "C66770", id_vars = oak_id_vars() ) %>% # Map VSLOC from TEMPLOC using assign_ct assign_ct( raw_dat = vs_raw, raw_var = "IT.TEMP_LOC", tgt_var = "VSLOC", ct_spec = study_ct, ct_clst = "C74456", id_vars = oak_id_vars() ) %>% # Create VSSTRESC by converting VSORRES from F to C mutate(VSSTRESC = as.character(sprintf("%.2f", (as.numeric(VSORRES) - 32) * 5 / 9))) %>% # Map VSSTRESU using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "IT.TEMP", tgt_var = "VSSTRESU", tgt_val = "C", ct_spec = study_ct, ct_clst = "C66770", id_vars = oak_id_vars() ) # Map topic variable HEIGHT from raw variable IT.HEIGHT_VSORRRES and its qualifiers. vs_height <- hardcode_ct( raw_dat = vs_raw, raw_var = "IT.HEIGHT_VSORRES", tgt_var = "VSTESTCD", tgt_val = "HEIGHT", ct_spec = study_ct, ct_clst = "C66741" ) %>% dplyr::filter(!is.na(.data$VSTESTCD)) %>% # Map VSTEST using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "IT.HEIGHT_VSORRES", tgt_var = "VSTEST", tgt_val = "Height", ct_spec = study_ct, ct_clst = "C67153", id_vars = oak_id_vars() ) %>% # Map VSORRES using assign_no_ct algorithm assign_no_ct( raw_dat = vs_raw, raw_var = "IT.HEIGHT_VSORRES", tgt_var = "VSORRES", id_vars = oak_id_vars() ) %>% # Map VSORRESU using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "IT.HEIGHT_VSORRES", tgt_var = "VSORRESU", tgt_val = "in", ct_spec = study_ct, ct_clst = "C66770", id_vars = oak_id_vars() ) %>% # Create VSSRESC by converting VSORRES from in to cm mutate(VSSTRESC = as.character(sprintf("%.2f", as.numeric(VSORRES) * 2.54))) %>% # Map VSSTRESU using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "IT.HEIGHT_VSORRES", tgt_var = "VSSTRESU", tgt_val = "cm", ct_spec = study_ct, ct_clst = "C66770", id_vars = oak_id_vars() ) # Map topic variable WEIGHT from raw variable IT.WEIGHT and its qualifiers. vs_weight <- hardcode_ct( raw_dat = vs_raw, raw_var = "IT.WEIGHT", tgt_var = "VSTESTCD", tgt_val = "WEIGHT", ct_spec = study_ct, ct_clst = "C66741" ) %>% dplyr::filter(!is.na(.data$VSTESTCD)) %>% # Map VSTEST using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "IT.WEIGHT", tgt_var = "VSTEST", tgt_val = "Weight", ct_spec = study_ct, ct_clst = "C67153", id_vars = oak_id_vars() ) %>% # Map VSORRES using assign_no_ct algorithm assign_no_ct( raw_dat = vs_raw, raw_var = "IT.WEIGHT", tgt_var = "VSORRES", id_vars = oak_id_vars() ) %>% # Map VSORRESU using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "IT.WEIGHT", tgt_var = "VSORRESU", tgt_val = "LB", ct_spec = study_ct, ct_clst = "C66770", id_vars = oak_id_vars() ) %>% # Create VSSTRESC by converting VSORRES from LB to KG mutate(VSSTRESC = as.character(sprintf("%.2f", as.numeric(VSORRES) / 2.20462))) %>% # Map VSSTRESU using hardcode_ct algorithm hardcode_ct( raw_dat = vs_raw, raw_var = "IT.WEIGHT", tgt_var = "VSSTRESU", tgt_val = "kg", ct_spec = study_ct, ct_clst = "C66770", id_vars = oak_id_vars() ) ``` Now that all the topic variable and its qualifier mappings are complete, combine all the datasets and proceed with mapping qualifiers, identifiers and timing variables applicable to all topic variables. ```{r} # Combine all the topic variables into a single data frame. vs_combined <- dplyr::bind_rows( vs_diabp, vs_height, vs_pulse, vs_sysbp, vs_temp, vs_weight ) ``` ```{r} # Map qualifiers common to all topic variables vs <- vs_combined %>% # Map VSDTC using assign_ct algorithm assign_datetime( raw_dat = vs_raw, raw_var = c("VTLD"), tgt_var = "VSDTC", raw_fmt = c(list(c("d-m-y", "dd-mmm-yyyy"))) ) %>% # Map VSTPT from TMPTC using assign_ct assign_ct( raw_dat = vs_raw, raw_var = "TMPTC", tgt_var = "VSTPT", ct_spec = study_ct, ct_clst = "TPT", id_vars = oak_id_vars() ) %>% # Map VSTPTNUM from TMPTC using assign_ct assign_ct( raw_dat = vs_raw, raw_var = "TMPTC", tgt_var = "VSTPTNUM", ct_spec = study_ct, ct_clst = "TPTNUM", id_vars = oak_id_vars() ) %>% # Map VISIT from INSTANCE using assign_ct assign_ct( raw_dat = vs_raw, raw_var = "INSTANCE", tgt_var = "VISIT", ct_spec = study_ct, ct_clst = "VISIT", id_vars = oak_id_vars() ) %>% # Map VISITNUM from INSTANCE using assign_ct assign_ct( raw_dat = vs_raw, raw_var = "INSTANCE", tgt_var = "VISITNUM", ct_spec = study_ct, ct_clst = "VISITNUM", id_vars = oak_id_vars() ) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(vs) ``` ## Create SDTM derived variables {#derivedvars} Create derived variables applicable to all topic variables. ```{r} vs <- vs %>% dplyr::mutate( STUDYID = "CDISCPILOT01", DOMAIN = "VS", VSCAT = "VITAL SIGNS", USUBJID = paste0("01", "-", .data$patient_number), VSSTRESC = ifelse(is.na(VSSTRESC), VSORRES, VSSTRESC), VSSTRESN = as.numeric(VSSTRESC), VSSTRESU = ifelse(is.na(VSSTRESU), VSORRESU, VSSTRESU), VSELTM = ifelse(is.na(VSTPT), NA, paste0("PT", readr::parse_number(VSTPT), "M")), VSTPTREF = ifelse(is.na(VSPOS), NA, paste("PATIENT", VSPOS)) ) %>% arrange(USUBJID, VSTESTCD, as.numeric(VISITNUM), as.numeric(VSTPTNUM)) %>% derive_seq( tgt_var = "VSSEQ", rec_vars = c("USUBJID", "VSTESTCD") ) %>% derive_study_day( sdtm_in = ., dm_domain = dm, tgdt = "VSDTC", refdt = "RFXSTDTC", study_day_var = "VSDY" ) %>% dplyr::select("STUDYID", "DOMAIN", "USUBJID", "VSSEQ", "VSTESTCD", "VSTEST", "VSPOS", "VSORRES", "VSORRESU", "VSSTRESC", "VSSTRESN", "VSSTRESU", "VSLOC", "VISITNUM", "VISIT", "VSDTC", "VSDY", "VSTPT", "VSTPTNUM", "VSELTM", "VSTPTREF") ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(vs) ``` ## Add Labels and Attributes {#attributes} Yet to be developed. Please refer to `{metatools}` package to investigate options.