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Creating an Findings SDTM domain

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 the “Creating an Interventions Domain” article, 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 (OID - vs_raw), Vital Signs - Treatment (OID - vs_t_raw)) can be stacked together before processing.

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:

vs_raw <- read.csv(system.file("raw_data/vitals_raw_data.csv",
  package = "sdtm.oak"
))

PATNUM	FORML	ASMNTDN	TMPTC	VTLD	VTLTM	SUBPOS	SYS_BP	DIA_BP	PULSE	RESPRT	TEMP	TEMPLOC	OXY_SAT	LAT	LOC
375	Vital Signs	0	Pre-dose	16-May-15	7:25	PRONE	158	92	63	17	40.48	SKIN	98	RIGHT	FINGER
375	Vital Signs	0	Post-dose	16-May-15	10:25	SEMI-RECUMBENT	94	78	76	20	36.75	TYMPANIC MEMBRANE	99	LEFT	FINGER
375	Vital Signs	0		6-May-18	2:01	PRONE	117	62	66	15	29.45	ORAL CAVITY	96	LEFT	FINGER
376	Vital Signs	1					NA	NA	NA	NA	NA		NA
376	Vital Signs	0	Pre-dose	23-Oct-08	1:19	PRONE	85	68	73	21	38.25	AXILLA	93	RIGHT	FINGER
376	Vital Signs	0	Post-dose	23-Oct-08	3:19	PRONE	126	81	56	18	38.08	TYMPANIC MEMBRANE	93	LEFT	FINGER

Create oak_id_vars

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

oak_id	raw_source	patient_number	PATNUM	FORML	SYS_BP	DIA_BP
1	vitals	375	375	Vital Signs	158	92
2	vitals	375	375	Vital Signs	94	78
3	vitals	375	375	Vital Signs	117	62
4	vitals	376	376	Vital Signs	NA	NA
5	vitals	376	376	Vital Signs	85	68
6	vitals	376	376	Vital Signs	126	81

Read in the DM domain

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(system.file("raw_data/sdtm_ct.csv",
  package = "sdtm.oak"
))

codelist_code	term_code	term_value	collected_value	term_preferred_term	term_synonyms
C66726	C25158	CAPSULE	Capsule	Capsule Dosage Form	cap
C66726	C25394	PILL	Pill	Pill Dosage Form
C66726	C29167	LOTION	Lotion	Lotion Dosage Form
C66726	C42887	AEROSOL	Aerosol	Aerosol Dosage Form	aer
C66726	C42944	INHALANT	Inhalant	Inhalant Dosage Form
C66726	C42946	INJECTION	Injection	Injectable Dosage Form
C66726	C42953	LIQUID	Liquid	Liquid Dosage Form
C66726	C42998	TABLET	Tablet	Tablet Dosage Form	tab
C66728	C25629	BEFORE	Prior	Prior
C66728	C53279	ONGOING	Continue	Continue	Continuous

Map Topic Variable

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

# Map topic variable SYSBP and its qualifiers.
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))

oak_id	raw_source	patient_number	VSTESTCD
1	vitals	375	SYSBP
2	vitals	375	SYSBP
3	vitals	375	SYSBP
5	vitals	376	SYSBP
6	vitals	376	SYSBP

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 topic variable SYSBP and its qualifiers.
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()
  )

oak_id	raw_source	patient_number	VSTESTCD	VSTEST	VSORRES	VSORRESU	VSPOS
1	vitals	375	SYSBP	Systolic Blood Pressure	158	mmHg	PRONE
2	vitals	375	SYSBP	Systolic Blood Pressure	94	mmHg	SEMI-RECUMBENT
3	vitals	375	SYSBP	Systolic Blood Pressure	117	mmHg	PRONE
5	vitals	376	SYSBP	Systolic Blood Pressure	85	mmHg	PRONE
6	vitals	376	SYSBP	Systolic Blood Pressure	126	mmHg	PRONE

Repeat Map Topic and Map Rest

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

# Map topic variable DIABP and its qualifiers.
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 topic variable RESP from the raw variable RESPRT and its qualifiers.
vs_resp <-
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "RESPRT",
    tgt_var = "VSTESTCD",
    tgt_val = "RESP",
    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 = "RESPRT",
    tgt_var = "VSTEST",
    tgt_val = "Respiratory 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 = "RESPRT",
    tgt_var = "VSORRES",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "RESPRT",
    tgt_var = "VSORRESU",
    tgt_val = "breaths/min",
    ct_spec = study_ct,
    ct_clst = "C66770",
    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 = "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 = "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 = "TEMP",
    tgt_var = "VSORRES",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "TEMP",
    tgt_var = "VSORRESU",
    tgt_val = "C",
    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 = "TEMPLOC",
    tgt_var = "VSLOC",
    ct_spec = study_ct,
    ct_clst = "C74456",
    id_vars = oak_id_vars()
  )

# Map topic variable OXYSAT from raw variable OXY_SAT and its qualifiers.
vs_oxysat <-
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "OXY_SAT",
    tgt_var = "VSTESTCD",
    tgt_val = "OXYSAT",
    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 = "OXY_SAT",
    tgt_var = "VSTEST",
    tgt_val = "Oxygen Saturation",
    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 = "OXY_SAT",
    tgt_var = "VSORRES",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSORRESU using hardcode_ct algorithm
  hardcode_ct(
    raw_dat = vs_raw,
    raw_var = "OXY_SAT",
    tgt_var = "VSORRESU",
    tgt_val = "%",
    ct_spec = study_ct,
    ct_clst = "C66770",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSLAT using assign_ct from raw variable LAT
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "LAT",
    tgt_var = "VSLAT",
    ct_spec = study_ct,
    ct_clst = "C99073",
    id_vars = oak_id_vars()
  ) %>%
  # Map VSLOC using assign_ct from raw variable LOC
  assign_ct(
    raw_dat = vs_raw,
    raw_var = "LOC",
    tgt_var = "VSLOC",
    ct_spec = study_ct,
    ct_clst = "C74456",
    id_vars = oak_id_vars()
  )

# Map topic variable VSALL from raw variable ASMNTDN with the logic if ASMNTDN  == 1 then VSTESTCD = VSALL
vs_vsall <-
  hardcode_ct(
    raw_dat = condition_add(vs_raw, ASMNTDN == 1L),
    raw_var = "ASMNTDN",
    tgt_var = "VSTESTCD",
    tgt_val = "VSALL",
    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 = "ASMNTDN",
    tgt_var = "VSTEST",
    tgt_val = "Vital Signs",
    ct_spec = study_ct,
    ct_clst = "C67153",
    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 and map qualifiers
# applicable to all topic variables
vs <- dplyr::bind_rows(
  vs_vsall, vs_sysbp, vs_diabp, vs_pulse, vs_resp,
  vs_temp, vs_oxysat
) %>%
  # Map qualifiers common to all topic variables
  # Map VSDTC using assign_ct algorithm
  assign_datetime(
    raw_dat = vs_raw,
    raw_var = c("VTLD", "VTLTM"),
    tgt_var = "VSDTC",
    raw_fmt = c(list(c("d-m-y", "dd-mmm-yyyy")), "H:M")
  ) %>%
  # 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()
  )

oak_id	raw_source	patient_number	VSTESTCD	VSTEST	VSORRES	VSORRESU	VSPOS	VSLAT	VSDTC	VSTPT	VSTPTNUM	VISIT	VISITNUM
1	vitals	375	SYSBP	Systolic Blood Pressure	158.00	mmHg	PRONE	NA	2015-05-16T07:25	PREDOSE	1	VISIT1	VISIT1
1	vitals	375	DIABP	Diastolic Blood Pressure	92.00	mmHg	PRONE	NA	2015-05-16T07:25	PREDOSE	1	VISIT1	VISIT1
1	vitals	375	PULSE	Pulse Rate	63.00	beats/min	NA	NA	2015-05-16T07:25	PREDOSE	1	VISIT1	VISIT1
1	vitals	375	RESP	Respiratory Rate	17.00	breaths/min	NA	NA	2015-05-16T07:25	PREDOSE	1	VISIT1	VISIT1
1	vitals	375	TEMP	Temperature	40.48	C	NA	NA	2015-05-16T07:25	PREDOSE	1	VISIT1	VISIT1
1	vitals	375	OXYSAT	Oxygen Saturation	98.00	%	NA	RIGHT	2015-05-16T07:25	PREDOSE	1	VISIT1	VISIT1
2	vitals	375	SYSBP	Systolic Blood Pressure	94.00	mmHg	SEMI-RECUMBENT	NA	2015-05-16T10:25	POSTDOSE	2	VISIT1	VISIT1
2	vitals	375	DIABP	Diastolic Blood Pressure	78.00	mmHg	SEMI-RECUMBENT	NA	2015-05-16T10:25	POSTDOSE	2	VISIT1	VISIT1
2	vitals	375	PULSE	Pulse Rate	76.00	beats/min	NA	NA	2015-05-16T10:25	POSTDOSE	2	VISIT1	VISIT1
2	vitals	375	RESP	Respiratory Rate	20.00	breaths/min	NA	NA	2015-05-16T10:25	POSTDOSE	2	VISIT1	VISIT1

Create SDTM derived variables

Create derived variables applicable to all topic variables.

vs <- vs %>%
  dplyr::mutate(
    STUDYID = "test_study",
    DOMAIN = "VS",
    VSCAT = "VITAL SIGNS",
    USUBJID = paste0("test_study", "-", .data$patient_number)
  ) %>%
  # derive_seq(tgt_var = "VSSEQ",
  #            rec_vars= c("USUBJID", "VSTRT")) %>%
  derive_study_day(
    sdtm_in = .,
    dm_domain = dm,
    tgdt = "VSDTC",
    refdt = "RFXSTDTC",
    study_day_var = "VSDY"
  ) %>%
  dplyr::select("STUDYID", "DOMAIN", "USUBJID", everything())

STUDYID	DOMAIN	USUBJID	VSTESTCD	VSTEST	VSORRES	VSORRESU	VSPOS	VSLAT	VSTPT	VSTPTNUM	VISIT	VISITNUM	VSDTC	VSDY
test_study	VS	test_study-375	SYSBP	Systolic Blood Pressure	158.00	mmHg	PRONE	NA	PREDOSE	1	VISIT1	VISIT1	2015-05-16	-2890
test_study	VS	test_study-375	DIABP	Diastolic Blood Pressure	92.00	mmHg	PRONE	NA	PREDOSE	1	VISIT1	VISIT1	2015-05-16	-2890
test_study	VS	test_study-375	PULSE	Pulse Rate	63.00	beats/min	NA	NA	PREDOSE	1	VISIT1	VISIT1	2015-05-16	-2890
test_study	VS	test_study-375	RESP	Respiratory Rate	17.00	breaths/min	NA	NA	PREDOSE	1	VISIT1	VISIT1	2015-05-16	-2890
test_study	VS	test_study-375	TEMP	Temperature	40.48	C	NA	NA	PREDOSE	1	VISIT1	VISIT1	2015-05-16	-2890
test_study	VS	test_study-375	OXYSAT	Oxygen Saturation	98.00	%	NA	RIGHT	PREDOSE	1	VISIT1	VISIT1	2015-05-16	-2890
test_study	VS	test_study-375	SYSBP	Systolic Blood Pressure	94.00	mmHg	SEMI-RECUMBENT	NA	POSTDOSE	2	VISIT1	VISIT1	2015-05-16	-2890
test_study	VS	test_study-375	DIABP	Diastolic Blood Pressure	78.00	mmHg	SEMI-RECUMBENT	NA	POSTDOSE	2	VISIT1	VISIT1	2015-05-16	-2890
test_study	VS	test_study-375	PULSE	Pulse Rate	76.00	beats/min	NA	NA	POSTDOSE	2	VISIT1	VISIT1	2015-05-16	-2890
test_study	VS	test_study-375	RESP	Respiratory Rate	20.00	breaths/min	NA	NA	POSTDOSE	2	VISIT1	VISIT1	2015-05-16	-2890

Add Labels and Attributes

Yet to be developed.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.