Day 8: Complex SDTM Domains - LB (Lab Results)

Findings Class with Unit Standardization

1 Learning Objectives

By the end of Day 8, you will be able to:

Understand the LB (Laboratory) domain structure and its role as a Findings class domain
Identify and explain the key LB variables: LBTESTCD, LBORRES, LBORRESU, LBSTRESC, LBSTRESN, LBSTRESU
Perform unit conversions (e.g., mg/dL → mmol/L) for standardized results
Derive reference range flags (LBNRIND) and understand normal range variables
Create a complete LB domain from simulated raw data using dplyr and tidyr

2 Introduction to the LB Domain

2.1 What is the LB Domain?

The LB (Laboratory) domain is one of the most complex and data-rich domains in SDTM. It belongs to the Findings class of SDTM domains, which are designed to capture observations or measurements collected during a study.

Findings Class Characteristics

Findings domains share a common structure:

–TESTCD: Short name for the test (e.g., “ALT”, “GLUC”)
–TEST: Full descriptive name (e.g., “Alanine Aminotransferase”, “Glucose”)
–ORRES: Original result as collected (character)
–ORRESU: Original unit of the result
–STRESC: Standardized result in character format
–STRESN: Standardized result in numeric format
–STRESU: Standardized unit

2.2 Why is LB Important?

Laboratory data is fundamental to clinical trials because:

It provides objective measurements of safety (liver enzymes, kidney function)
It helps assess efficacy (biomarkers, disease markers)
Regulatory agencies require standardized lab data for safety reviews
Lab abnormalities often determine eligibility and safety signals

2.3 The LB Domain Structure

┌──────────────────────────────────────────────────────────────────────────────┐
│                        LB DOMAIN - KEY VARIABLES                             │
├──────────────────────────────────────────────────────────────────────────────┤
│  IDENTIFIER VARIABLES                                                        │
│    STUDYID    = Study identifier                                             │
│    USUBJID    = Unique subject identifier (links to DM)                      │
│    LBSEQ      = Sequence number within subject                               │
├──────────────────────────────────────────────────────────────────────────────┤
│  TOPIC VARIABLE                                                              │
│    LBTESTCD   = Lab test short name (e.g., "ALT", "BILI", "GLUC")            │
│    LBTEST     = Lab test full name (e.g., "Alanine Aminotransferase")        │
│    LBCAT      = Category (e.g., "CHEMISTRY", "HEMATOLOGY")                   │
├──────────────────────────────────────────────────────────────────────────────┤
│  RESULT VARIABLES (Original)                                                 │
│    LBORRES    = Result as originally collected (character)                   │
│    LBORRESU   = Unit of original result                                      │
├──────────────────────────────────────────────────────────────────────────────┤
│  RESULT VARIABLES (Standardized)                                             │
│    LBSTRESC   = Standardized result (character)                              │
│    LBSTRESN   = Standardized result (numeric)                                │
│    LBSTRESU   = Standardized unit                                            │
├──────────────────────────────────────────────────────────────────────────────┤
│  REFERENCE RANGE VARIABLES                                                   │
│    LBSTNRLO   = Standard normal range lower limit                            │
│    LBSTNRHI   = Standard normal range upper limit                            │
│    LBNRIND    = Normal range indicator (LOW/NORMAL/HIGH)                     │
├──────────────────────────────────────────────────────────────────────────────┤
│  TIMING VARIABLES                                                            │
│    VISITNUM   = Visit number                                                 │
│    VISIT      = Visit name (e.g., "SCREENING", "WEEK 2")                     │
│    LBDTC      = Date/time of specimen collection (ISO 8601)                  │
│    LBDY       = Study day of specimen collection                             │
└──────────────────────────────────────────────────────────────────────────────┘

3 Package Installation & Loading

3.1 Required Packages

Package	Purpose
`dplyr`	Data manipulation (filter, mutate, joins)
`tidyr`	Data reshaping (pivot operations)
`pharmaversesdtm`	Example SDTM datasets including LB

3.2 Install Packages (if needed)

# Install packages if not already installed
if (!requireNamespace("dplyr", quietly = TRUE)) suppressMessages(install.packages("dplyr"))
if (!requireNamespace("tidyr", quietly = TRUE)) suppressMessages(install.packages("tidyr"))
if (!requireNamespace("pharmaversesdtm", quietly = TRUE)) suppressMessages(install.packages("pharmaversesdtm"))

3.3 Load Packages

library(dplyr)
library(tidyr)
library(pharmaversesdtm)

4 Exploring pharmaversesdtm LB Data

Let’s start by loading and exploring the LB domain from pharmaversesdtm. This is the same data used by production teams in the pharmaverse community when developing the admiral package.

4.1 Load the LB Domain

# Load LB domain from pharmaversesdtm
data("lb", package = "pharmaversesdtm")

# Quick overview
cat("LB domain dimensions:", nrow(lb), "rows x", ncol(lb), "columns\n")

LB domain dimensions: 59580 rows x 23 columns

cat("Number of unique subjects:", n_distinct(lb$USUBJID), "\n")

Number of unique subjects: 254

cat("Number of unique tests:", n_distinct(lb$LBTESTCD), "\n")

Number of unique tests: 47

4.2 Explore LB Structure

# View the structure of the LB domain
dplyr::glimpse(lb)

Rows: 59,580
Columns: 23
$ STUDYID  <chr> "CDISCPILOT01", "CDISCPILOT01", "CDISCPILOT01", "CDISCPILOT01…
$ DOMAIN   <chr> "LB", "LB", "LB", "LB", "LB", "LB", "LB", "LB", "LB", "LB", "…
$ USUBJID  <chr> "01-701-1015", "01-701-1015", "01-701-1015", "01-701-1015", "…
$ LBSEQ    <dbl> 1, 39, 74, 104, 134, 164, 199, 229, 259, 294, 2, 40, 75, 105,…
$ LBTESTCD <chr> "ALB", "ALB", "ALB", "ALB", "ALB", "ALB", "ALB", "ALB", "ALB"…
$ LBTEST   <chr> "Albumin", "Albumin", "Albumin", "Albumin", "Albumin", "Album…
$ LBCAT    <chr> "CHEMISTRY", "CHEMISTRY", "CHEMISTRY", "CHEMISTRY", "CHEMISTR…
$ LBORRES  <chr> "3.8", "3.9", "3.8", "3.7", "3.8", "3.8", "3.7", "3.7", "3.8"…
$ LBORRESU <chr> "g/dL", "g/dL", "g/dL", "g/dL", "g/dL", "g/dL", "g/dL", "g/dL…
$ LBORNRLO <chr> "3.3", "3.3", "3.3", "3.3", "3.3", "3.3", "3.3", "3.3", "3.3"…
$ LBORNRHI <chr> "4.9", "4.9", "4.9", "4.9", "4.9", "4.9", "4.9", "4.9", "4.9"…
$ LBSTRESC <chr> "38", "39", "38", "37", "38", "38", "37", "37", "38", "38", "…
$ LBSTRESN <dbl> 38, 39, 38, 37, 38, 38, 37, 37, 38, 38, 34, 50, 41, 43, 47, 5…
$ LBSTRESU <chr> "g/L", "g/L", "g/L", "g/L", "g/L", "g/L", "g/L", "g/L", "g/L"…
$ LBSTNRLO <dbl> 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 35, 35, 35, 35, 35, 3…
$ LBSTNRHI <dbl> 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 115, 115, 115, 115, 1…
$ LBNRIND  <chr> "NORMAL", "NORMAL", "NORMAL", "NORMAL", "NORMAL", "NORMAL", "…
$ LBBLFL   <chr> "Y", NA, NA, NA, NA, NA, NA, NA, NA, NA, "Y", NA, NA, NA, NA,…
$ VISITNUM <dbl> 1, 4, 5, 7, 8, 9, 10, 11, 12, 13, 1, 4, 5, 7, 8, 9, 10, 11, 1…
$ VISIT    <chr> "SCREENING 1", "WEEK 2", "WEEK 4", "WEEK 6", "WEEK 8", "WEEK …
$ VISITDY  <dbl> -7, 14, 28, 42, 56, 84, 112, 140, 168, 182, -7, 14, 28, 42, 5…
$ LBDTC    <chr> "2013-12-26T14:45", "2014-01-16T13:17", "2014-01-30T08:50", "…
$ LBDY     <dbl> -7, 15, 29, 42, 63, 84, 126, 140, 168, 182, -7, 15, 29, 42, 6…

4.3 Key LB Variables Explained

Let’s examine some critical LB variables:

# View unique lab tests
lb %>%
  distinct(LBTESTCD, LBTEST, LBCAT) %>%
  arrange(LBCAT, LBTESTCD) %>%
  head(15)

# A tibble: 15 × 3
   LBTESTCD LBTEST                     LBCAT    
   <chr>    <chr>                      <chr>    
 1 ALB      Albumin                    CHEMISTRY
 2 ALP      Alkaline Phosphatase       CHEMISTRY
 3 ALT      Alanine Aminotransferase   CHEMISTRY
 4 AST      Aspartate Aminotransferase CHEMISTRY
 5 BILI     Bilirubin                  CHEMISTRY
 6 BUN      Blood Urea Nitrogen        CHEMISTRY
 7 CA       Calcium                    CHEMISTRY
 8 CHOL     Cholesterol                CHEMISTRY
 9 CK       Creatine Kinase            CHEMISTRY
10 CL       Chloride                   CHEMISTRY
11 CREAT    Creatinine                 CHEMISTRY
12 GGT      Gamma Glutamyl Transferase CHEMISTRY
13 GLUC     Glucose                    CHEMISTRY
14 K        Potassium                  CHEMISTRY
15 PHOS     Phosphate                  CHEMISTRY

Understanding Test Codes

Notice how LBTESTCD is a short, standardized code while LBTEST is the full descriptive name. This follows CDISC conventions where:

Short codes (LBTESTCD) enable efficient data processing
Full names (LBTEST) provide human-readable descriptions
Categories (LBCAT) group related tests (CHEMISTRY, HEMATOLOGY, etc.)

5 Understanding Original vs Standardized Results

One of the most important concepts in the LB domain is the distinction between original and standardized results.

5.1 Why Standardization Matters

Labs from different sites may report the same test in different units:

Site A reports Glucose as 90 mg/dL
Site B reports Glucose as 5.0 mmol/L

Both values are the same measurement, just in different units! SDTM requires us to standardize these values so they can be analyzed together.

5.2 The Result Variable Pairs

Original Variable	Standardized Variable	Description
`LBORRES`	`LBSTRESC`	Result (character format)
-	`LBSTRESN`	Result (numeric format)
`LBORRESU`	`LBSTRESU`	Unit of measurement

5.3 Viewing Original vs Standardized Values

# Compare original and standardized values for a specific test
lb %>%
  filter(LBTESTCD == "GLUC") %>%
  select(USUBJID, VISIT, LBORRES, LBORRESU, LBSTRESC, LBSTRESN, LBSTRESU) %>%
  head(10)

# A tibble: 10 × 7
   USUBJID     VISIT       LBORRES LBORRESU LBSTRESC LBSTRESN LBSTRESU
   <chr>       <chr>       <chr>   <chr>    <chr>       <dbl> <chr>   
 1 01-701-1015 SCREENING 1 85      mg/dL    4.71835      4.72 mmol/L  
 2 01-701-1015 WEEK 2      84      mg/dL    4.66284      4.66 mmol/L  
 3 01-701-1015 WEEK 4      79      mg/dL    4.38529      4.39 mmol/L  
 4 01-701-1015 WEEK 6      92      mg/dL    5.10692      5.11 mmol/L  
 5 01-701-1015 WEEK 8      82      mg/dL    4.55182      4.55 mmol/L  
 6 01-701-1015 WEEK 12     87      mg/dL    4.82937      4.83 mmol/L  
 7 01-701-1015 WEEK 16     86      mg/dL    4.77386      4.77 mmol/L  
 8 01-701-1015 WEEK 20     88      mg/dL    4.88488      4.88 mmol/L  
 9 01-701-1015 WEEK 24     81      mg/dL    4.49631      4.50 mmol/L  
10 01-701-1015 WEEK 26     92      mg/dL    5.10692      5.11 mmol/L

6 Unit Conversion: A Hands-On Example

Let’s create a practical example of unit conversion. This is one of the most common tasks when building an LB domain from raw lab data.

6.1 Common Lab Unit Conversions

Test	Original Unit	Standard Unit	Conversion Factor
Glucose	mg/dL	mmol/L	÷ 18.02
Creatinine	mg/dL	µmol/L	× 88.42
Bilirubin	mg/dL	µmol/L	× 17.10
Cholesterol	mg/dL	mmol/L	÷ 38.67

Reference for Unit Conversions

For a comprehensive list of clinical lab unit conversions, see:

6.2 Step-by-Step: Creating Standardized Results

Let’s simulate a scenario where we receive raw lab data and need to standardize it.

# Create simulated raw lab data with mixed units
raw_lab <- tibble::tribble(
  ~USUBJID,           ~LBTESTCD, ~LBTEST,              ~LBORRES, ~LBORRESU, ~VISITNUM, ~VISIT,
  "CDISC01-001-001",  "GLUC",    "Glucose",            "95",     "mg/dL",   1,         "BASELINE",
  "CDISC01-001-001",  "GLUC",    "Glucose",            "5.1",    "mmol/L",  2,         "WEEK 2",
  "CDISC01-001-001",  "CREAT",   "Creatinine",         "1.2",    "mg/dL",   1,         "BASELINE",
  "CDISC01-001-001",  "CREAT",   "Creatinine",         "98",     "umol/L",  2,         "WEEK 2",
  "CDISC01-001-002",  "GLUC",    "Glucose",            "102",    "mg/dL",   1,         "BASELINE",
  "CDISC01-001-002",  "GLUC",    "Glucose",            "88",     "mg/dL",   2,         "WEEK 2",
  "CDISC01-001-002",  "BILI",    "Bilirubin Total",    "0.8",    "mg/dL",   1,         "BASELINE",
  "CDISC01-001-002",  "BILI",    "Bilirubin Total",    "1.1",    "mg/dL",   2,         "WEEK 2"
)

cat("Raw lab data with mixed units:\n")

Raw lab data with mixed units:

print(raw_lab)

# A tibble: 8 × 7
  USUBJID         LBTESTCD LBTEST          LBORRES LBORRESU VISITNUM VISIT   
  <chr>           <chr>    <chr>           <chr>   <chr>       <dbl> <chr>   
1 CDISC01-001-001 GLUC     Glucose         95      mg/dL           1 BASELINE
2 CDISC01-001-001 GLUC     Glucose         5.1     mmol/L          2 WEEK 2  
3 CDISC01-001-001 CREAT    Creatinine      1.2     mg/dL           1 BASELINE
4 CDISC01-001-001 CREAT    Creatinine      98      umol/L          2 WEEK 2  
5 CDISC01-001-002 GLUC     Glucose         102     mg/dL           1 BASELINE
6 CDISC01-001-002 GLUC     Glucose         88      mg/dL           2 WEEK 2  
7 CDISC01-001-002 BILI     Bilirubin Total 0.8     mg/dL           1 BASELINE
8 CDISC01-001-002 BILI     Bilirubin Total 1.1     mg/dL           2 WEEK 2

6.3 Create a Unit Conversion Function

# Define a reusable conversion function
convert_lab_units <- function(testcd, value, from_unit, to_unit) {
  # Convert character value to numeric
  val <- as.numeric(value)
  
  # Define conversion factors (from original to standard)
  # Standard units: Glucose = mmol/L, Creatinine = umol/L, Bilirubin = umol/L
  
  result <- case_when(
    # Glucose: mg/dL to mmol/L
    testcd == "GLUC" & from_unit == "mg/dL" & to_unit == "mmol/L" ~ val / 18.02,
    
    # Glucose: already in mmol/L
    testcd == "GLUC" & from_unit == "mmol/L" & to_unit == "mmol/L" ~ val,
    
    # Creatinine: mg/dL to umol/L
    testcd == "CREAT" & from_unit == "mg/dL" & to_unit == "umol/L" ~ val * 88.42,
    
    # Creatinine: already in umol/L
    testcd == "CREAT" & from_unit == "umol/L" & to_unit == "umol/L" ~ val,
    
    # Bilirubin: mg/dL to umol/L
    testcd == "BILI" & from_unit == "mg/dL" & to_unit == "umol/L" ~ val * 17.10,
    
    # Default: no conversion
    TRUE ~ val
  )
  
  return(round(result, 2))
}

6.4 Apply Unit Conversions

# Define standard units for each test
standard_units <- tibble::tribble(
  ~LBTESTCD, ~LBSTRESU,
  "GLUC",    "mmol/L",
  "CREAT",   "umol/L",
  "BILI",    "umol/L"
)

# Apply standardization
lab_standardized <- raw_lab %>%
  # Join to get target standard units
  left_join(standard_units, by = "LBTESTCD") %>%
  # Apply conversion
  mutate(
    LBSTRESN = convert_lab_units(LBTESTCD, LBORRES, LBORRESU, LBSTRESU),
    LBSTRESC = as.character(LBSTRESN)
  ) %>%
  # Reorder columns for clarity
  select(USUBJID, LBTESTCD, LBTEST, VISITNUM, VISIT, 
         LBORRES, LBORRESU, LBSTRESC, LBSTRESN, LBSTRESU)

cat("Standardized lab data:\n")

Standardized lab data:

print(lab_standardized)

# A tibble: 8 × 10
  USUBJID      LBTESTCD LBTEST VISITNUM VISIT LBORRES LBORRESU LBSTRESC LBSTRESN
  <chr>        <chr>    <chr>     <dbl> <chr> <chr>   <chr>    <chr>       <dbl>
1 CDISC01-001… GLUC     Gluco…        1 BASE… 95      mg/dL    5.27         5.27
2 CDISC01-001… GLUC     Gluco…        2 WEEK… 5.1     mmol/L   5.1          5.1 
3 CDISC01-001… CREAT    Creat…        1 BASE… 1.2     mg/dL    106.1      106.  
4 CDISC01-001… CREAT    Creat…        2 WEEK… 98      umol/L   98          98   
5 CDISC01-001… GLUC     Gluco…        1 BASE… 102     mg/dL    5.66         5.66
6 CDISC01-001… GLUC     Gluco…        2 WEEK… 88      mg/dL    4.88         4.88
7 CDISC01-001… BILI     Bilir…        1 BASE… 0.8     mg/dL    13.68       13.7 
8 CDISC01-001… BILI     Bilir…        2 WEEK… 1.1     mg/dL    18.81       18.8 
# ℹ 1 more variable: LBSTRESU <chr>

Key Learning Point

Notice how the original values are preserved (LBORRES, LBORRESU) while standardized values are derived (LBSTRESC, LBSTRESN, LBSTRESU). This is a fundamental SDTM principle: never lose the original data!

7 Reference Ranges and Normal Range Indicators

7.1 What are Reference Ranges?

Reference ranges (also called normal ranges) define the expected values for a lab test in a healthy population. Values outside this range may indicate:

LOW: Value below the normal range (potentially abnormal)
NORMAL: Value within expected range
HIGH: Value above the normal range (potentially abnormal)

7.2 Key Reference Range Variables

Variable	Description
`LBSTNRLO`	Standard normal range - lower limit
`LBSTNRHI`	Standard normal range - upper limit
`LBNRIND`	Normal range indicator (LOW/NORMAL/HIGH)
`LBORNRLO`	Original normal range - lower limit
`LBORNRHI`	Original normal range - upper limit

7.3 Deriving Reference Range Flags

# Define reference ranges (typically from lab vendor or specifications)
reference_ranges <- tibble::tribble(
  ~LBTESTCD, ~LBSTNRLO, ~LBSTNRHI, ~LBSTRESU,
  "GLUC",    3.9,       5.6,       "mmol/L",   # Fasting glucose
  "CREAT",   62,        106,       "umol/L",   # Creatinine (male)
  "BILI",    5.1,       17.0,      "umol/L"    # Total bilirubin
)

# Join reference ranges and derive LBNRIND
lab_with_ranges <- lab_standardized %>%
  left_join(
    reference_ranges %>% select(LBTESTCD, LBSTNRLO, LBSTNRHI),
    by = "LBTESTCD"
  ) %>%
  mutate(
    # Derive normal range indicator
    LBNRIND = case_when(
      is.na(LBSTRESN) ~ NA_character_,                    # Missing result
      LBSTRESN < LBSTNRLO ~ "LOW",                        # Below range
      LBSTRESN > LBSTNRHI ~ "HIGH",                       # Above range
      TRUE ~ "NORMAL"                                     # Within range
    )
  )

# Display results
lab_with_ranges %>%
  select(USUBJID, LBTESTCD, VISIT, LBSTRESN, LBSTRESU, 
         LBSTNRLO, LBSTNRHI, LBNRIND)

# A tibble: 8 × 8
  USUBJID         LBTESTCD VISIT    LBSTRESN LBSTRESU LBSTNRLO LBSTNRHI LBNRIND
  <chr>           <chr>    <chr>       <dbl> <chr>       <dbl>    <dbl> <chr>  
1 CDISC01-001-001 GLUC     BASELINE     5.27 mmol/L        3.9      5.6 NORMAL 
2 CDISC01-001-001 GLUC     WEEK 2       5.1  mmol/L        3.9      5.6 NORMAL 
3 CDISC01-001-001 CREAT    BASELINE   106.   umol/L       62      106   HIGH   
4 CDISC01-001-001 CREAT    WEEK 2      98    umol/L       62      106   NORMAL 
5 CDISC01-001-002 GLUC     BASELINE     5.66 mmol/L        3.9      5.6 HIGH   
6 CDISC01-001-002 GLUC     WEEK 2       4.88 mmol/L        3.9      5.6 NORMAL 
7 CDISC01-001-002 BILI     BASELINE    13.7  umol/L        5.1     17   NORMAL 
8 CDISC01-001-002 BILI     WEEK 2      18.8  umol/L        5.1     17   HIGH

Clinical Significance

The LBNRIND variable is critical for safety analyses. It helps identify:

Clinically significant abnormalities that may require action
Trends in lab values over time (e.g., worsening liver function)
Treatment-emergent abnormalities (normal at baseline, abnormal post-treatment)

8 LOINC Coding Concepts

8.1 What is LOINC?

LOINC (Logical Observation Identifiers Names and Codes) is a universal standard for identifying medical laboratory observations. While not always required in SDTM, it enhances interoperability.

8.2 How LOINC Integrates with SDTM

SDTM Variable	LOINC Equivalent
`LBLOINC`	LOINC code for the test
`LBTESTCD`	Mapped to LOINC component

8.3 Example LOINC Codes

Test	LBTESTCD	LOINC Code	Description
Glucose	GLUC	2339-0	Glucose [Mass/volume] in Blood
Creatinine	CREAT	2160-0	Creatinine [Mass/volume] in Serum or Plasma
ALT	ALT	1742-6	Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma

Learn More About LOINC

9 Complete Example: Building an LB Domain

Let’s put everything together and build a more complete LB domain from simulated raw data.

9.1 Step 1: Create Comprehensive Raw Data

# Create more comprehensive raw lab data
set.seed(42)

subjects <- c("CDISC01-001-001", "CDISC01-001-002", "CDISC01-001-003",
              "CDISC01-001-004", "CDISC01-001-005")
visits <- c("SCREENING", "BASELINE", "WEEK 2", "WEEK 4", "WEEK 8")
visit_nums <- c(-1, 1, 2, 3, 4)

# Generate raw lab data for 5 subjects across 5 visits
raw_lb_data <- expand_grid(
  USUBJID = subjects,
  VISIT_INFO = tibble(VISIT = visits, VISITNUM = visit_nums)
) %>%
  unnest(VISIT_INFO) %>%
  # Add lab tests
  crossing(
    tibble::tribble(
      ~LBTESTCD, ~LBTEST,                      ~LBCAT,      ~LBORRESU,
      "GLUC",    "Glucose",                    "CHEMISTRY", "mg/dL",
      "CREAT",   "Creatinine",                 "CHEMISTRY", "mg/dL",
      "ALT",     "Alanine Aminotransferase",   "CHEMISTRY", "U/L",
      "AST",     "Aspartate Aminotransferase", "CHEMISTRY", "U/L",
      "BILI",    "Bilirubin Total",            "CHEMISTRY", "mg/dL",
      "WBC",     "Leukocytes",                 "HEMATOLOGY","10^9/L",
      "RBC",     "Erythrocytes",               "HEMATOLOGY","10^12/L",
      "HGB",     "Hemoglobin",                 "HEMATOLOGY","g/dL",
      "PLT",     "Platelets",                  "HEMATOLOGY","10^9/L",
      "SODIUM",  "Sodium",                     "CHEMISTRY", "mmol/L"
    )
  ) %>%
  # Generate random values within typical ranges
  rowwise() %>%
  mutate(
    LBORRES = as.character(round(case_when(
      LBTESTCD == "GLUC"   ~ rnorm(1, 100, 15),
      LBTESTCD == "CREAT"  ~ rnorm(1, 1.0, 0.2),
      LBTESTCD == "ALT"    ~ rnorm(1, 30, 10),
      LBTESTCD == "AST"    ~ rnorm(1, 28, 8),
      LBTESTCD == "BILI"   ~ rnorm(1, 0.8, 0.3),
      LBTESTCD == "WBC"    ~ rnorm(1, 7.0, 2.0),
      LBTESTCD == "RBC"    ~ rnorm(1, 4.8, 0.5),
      LBTESTCD == "HGB"    ~ rnorm(1, 14.5, 1.5),
      LBTESTCD == "PLT"    ~ rnorm(1, 250, 50),
      LBTESTCD == "SODIUM" ~ rnorm(1, 140, 3),
      TRUE ~ NA_real_
    ), 2))
  ) %>%
  ungroup() %>%
  # Add STUDYID and sequence
  mutate(STUDYID = "CDISC01") %>%
  group_by(USUBJID) %>%
  mutate(LBSEQ = row_number()) %>%
  ungroup()

cat("Raw LB data created:", nrow(raw_lb_data), "records\n")

Raw LB data created: 250 records

cat("Tests per subject per visit:", n_distinct(raw_lb_data$LBTESTCD), "\n")

Tests per subject per visit: 10

9.2 Step 2: Apply Standardization

# Define standard units and reference ranges for all tests
lab_standards <- tibble::tribble(
  ~LBTESTCD, ~LBSTRESU,  ~LBSTNRLO, ~LBSTNRHI, ~CONV_FACTOR,
  "GLUC",    "mmol/L",   3.9,       5.6,       0.0555,    # mg/dL to mmol/L
  "CREAT",   "umol/L",   62,        106,       88.42,     # mg/dL to umol/L
  "ALT",     "U/L",      7,         56,        1.0,       # No conversion
  "AST",     "U/L",      10,        40,        1.0,       # No conversion
  "BILI",    "umol/L",   5.1,       17.0,      17.10,     # mg/dL to umol/L
  "WBC",     "10^9/L",   4.5,       11.0,      1.0,       # No conversion
  "RBC",     "10^12/L",  4.2,       5.4,       1.0,       # No conversion
  "HGB",     "g/dL",     12.0,      17.0,      1.0,       # No conversion
  "PLT",     "10^9/L",   150,       400,       1.0,       # No conversion
  "SODIUM",  "mmol/L",   136,       145,       1.0        # No conversion
)

# Create complete LB domain
lb_complete <- raw_lb_data %>%
  # Join standards
  left_join(lab_standards, by = "LBTESTCD") %>%
  # Calculate standardized numeric result
  mutate(
    LBSTRESN = round(as.numeric(LBORRES) * CONV_FACTOR, 2),
    LBSTRESC = as.character(LBSTRESN),
    # Derive normal range indicator
    LBNRIND = case_when(
      is.na(LBSTRESN) ~ NA_character_,
      LBSTRESN < LBSTNRLO ~ "LOW",
      LBSTRESN > LBSTNRHI ~ "HIGH",
      TRUE ~ "NORMAL"
    ),
    # Add specimen type (typically from raw data, simulated here)
    LBSPEC = "SERUM"
  ) %>%
  # Select and order columns per SDTM standard
  select(
    STUDYID, USUBJID, LBSEQ,
    LBTESTCD, LBTEST, LBCAT, LBSPEC,
    LBORRES, LBORRESU,
    LBSTRESC, LBSTRESN, LBSTRESU,
    LBSTNRLO, LBSTNRHI, LBNRIND,
    VISITNUM, VISIT
  ) %>%
  arrange(STUDYID, USUBJID, VISITNUM, LBTESTCD)

# Preview
cat("\nComplete LB domain preview:\n")


Complete LB domain preview:

head(lb_complete, 15)

# A tibble: 15 × 17
   STUDYID USUBJID  LBSEQ LBTESTCD LBTEST LBCAT LBSPEC LBORRES LBORRESU LBSTRESC
   <chr>   <chr>    <int> <chr>    <chr>  <chr> <chr>  <chr>   <chr>    <chr>   
 1 CDISC01 CDISC01…    11 ALT      Alani… CHEM… SERUM  19.97   U/L      19.97   
 2 CDISC01 CDISC01…    12 AST      Aspar… CHEM… SERUM  23.97   U/L      23.97   
 3 CDISC01 CDISC01…    13 BILI     Bilir… CHEM… SERUM  0.8     mg/dL    13.68   
 4 CDISC01 CDISC01…    14 CREAT    Creat… CHEM… SERUM  0.9     mg/dL    79.58   
 5 CDISC01 CDISC01…    15 GLUC     Gluco… CHEM… SERUM  98.71   mg/dL    5.48    
 6 CDISC01 CDISC01…    16 HGB      Hemog… HEMA… SERUM  13.3    g/dL     13.3    
 7 CDISC01 CDISC01…    17 PLT      Plate… HEMA… SERUM  254.24  10^9/L   254.24  
 8 CDISC01 CDISC01…    18 RBC      Eryth… HEMA… SERUM  4.08    10^12/L  4.08    
 9 CDISC01 CDISC01…    19 SODIUM   Sodium CHEM… SERUM  135.22  mmol/L   135.22  
10 CDISC01 CDISC01…    20 WBC      Leuko… HEMA… SERUM  9.17    10^9/L   9.17    
11 CDISC01 CDISC01…     1 ALT      Alani… CHEM… SERUM  33.63   U/L      33.63   
12 CDISC01 CDISC01…     2 AST      Aspar… CHEM… SERUM  25.77   U/L      25.77   
13 CDISC01 CDISC01…     3 BILI     Bilir… CHEM… SERUM  1.37    mg/dL    23.43   
14 CDISC01 CDISC01…     4 CREAT    Creat… CHEM… SERUM  1.14    mg/dL    100.8   
15 CDISC01 CDISC01…     5 GLUC     Gluco… CHEM… SERUM  103.09  mg/dL    5.72    
# ℹ 7 more variables: LBSTRESN <dbl>, LBSTRESU <chr>, LBSTNRLO <dbl>,
#   LBSTNRHI <dbl>, LBNRIND <chr>, VISITNUM <dbl>, VISIT <chr>

9.3 Step 3: Validate the LB Domain

# Validation checks
cat("=== LB Domain Validation ===\n\n")

=== LB Domain Validation ===

# Check 1: All required variables present
required_vars <- c("STUDYID", "USUBJID", "LBSEQ", "LBTESTCD", "LBTEST", 
                   "LBORRES", "LBORRESU", "LBSTRESN", "LBSTRESU")
present <- required_vars %in% names(lb_complete)
cat("Required variables check:\n")

Required variables check:

for (i in seq_along(required_vars)) {
  cat("  ", required_vars[i], ":", ifelse(present[i], "✓", "✗"), "\n")
}

   STUDYID : ✓ 
   USUBJID : ✓ 
   LBSEQ : ✓ 
   LBTESTCD : ✓ 
   LBTEST : ✓ 
   LBORRES : ✓ 
   LBORRESU : ✓ 
   LBSTRESN : ✓ 
   LBSTRESU : ✓

# Check 2: No missing USUBJID
cat("\nMissing USUBJID:", sum(is.na(lb_complete$USUBJID)), "\n")


Missing USUBJID: 0

# Check 3: Distribution of LBNRIND
cat("\nNormal Range Indicator distribution:\n")


Normal Range Indicator distribution:

table(lb_complete$LBNRIND, useNA = "ifany")


  HIGH    LOW NORMAL 
    24     12    214

# Check 4: Unique subjects and visits
cat("\nData coverage:\n")


Data coverage:

cat("  Unique subjects:", n_distinct(lb_complete$USUBJID), "\n")

  Unique subjects: 5

cat("  Unique visits:", n_distinct(lb_complete$VISIT), "\n")

  Unique visits: 5

cat("  Unique tests:", n_distinct(lb_complete$LBTESTCD), "\n")

  Unique tests: 10

cat("  Total records:", nrow(lb_complete), "\n")

  Total records: 250

10 Summary Statistics for LB Data

When working with lab data, it’s often useful to generate summary statistics.

# Summary by test and visit
lb_summary <- lb_complete %>%
  group_by(LBTESTCD, LBTEST, VISIT, LBSTRESU) %>%
  summarise(
    N = n(),
    Mean = round(mean(LBSTRESN, na.rm = TRUE), 2),
    SD = round(sd(LBSTRESN, na.rm = TRUE), 2),
    Min = round(min(LBSTRESN, na.rm = TRUE), 2),
    Max = round(max(LBSTRESN, na.rm = TRUE), 2),
    N_Low = sum(LBNRIND == "LOW", na.rm = TRUE),
    N_High = sum(LBNRIND == "HIGH", na.rm = TRUE),
    .groups = "drop"
  )

# Display chemistry panel at BASELINE
cat("Chemistry Panel Summary at BASELINE:\n")

Chemistry Panel Summary at BASELINE:

lb_summary %>%
  filter(VISIT == "BASELINE", LBTESTCD %in% c("GLUC", "CREAT", "ALT", "AST", "BILI")) %>%
  print()

# A tibble: 5 × 11
  LBTESTCD LBTEST     VISIT LBSTRESU     N  Mean    SD   Min    Max N_Low N_High
  <chr>    <chr>      <chr> <chr>    <int> <dbl> <dbl> <dbl>  <dbl> <int>  <int>
1 ALT      Alanine A… BASE… U/L          5 27.2  10.8  12.8   39.7      0      0
2 AST      Aspartate… BASE… U/L          5 26.6   5.4  21.0   35.3      0      0
3 BILI     Bilirubin… BASE… umol/L       5 20.2   7.16 11.1   30.3      0      4
4 CREAT    Creatinine BASE… umol/L       5 84    12.1  69.0  101.       0      0
5 GLUC     Glucose    BASE… mmol/L       5  4.95  0.56  4.51   5.72     0      1

11 Deliverable Summary

Today you completed the following:

Task	Status
Understood LB domain structure and Findings class	✓ Done
Explored key LB variables (LBTESTCD, LBORRES, LBSTRESN, etc.)	✓ Done
Performed unit conversions (mg/dL → mmol/L)	✓ Done
Derived reference range flags (LBNRIND)	✓ Done
Created a complete LB domain with 10+ tests	✓ Done
Generated summary statistics	✓ Done

12 Key Takeaways

LB is a Findings class domain - It follows the –TESTCD, –ORRES, –STRESC pattern shared by VS, LB, EG
Original vs Standardized - Always preserve original results while creating standardized versions
Unit conversions are critical - Labs from different sites may use different units
Reference ranges enable safety analysis - LBNRIND flags abnormal values
LOINC provides universal identification - Enhances data interoperability

13 Resources

CDISC SDTM Implementation Guide - LB Domain - Official LB specification
LOINC Official Website - Medical laboratory codes
NIH Unit Conversion Tables - Lab unit conversions
Pharmaverse.org - R packages for clinical data
sdtm.oak Documentation - SDTM creation package

14 What’s Next?

In Day 9, we will focus on VS (Vital Signs) & Repeated Measures:

Understanding visit-level data and multiple readings per timepoint
Working with positional variables (VSPOS, VSLOC)
Deriving statistics across multiple readings
Preparing for ADaM BDS structure (baseline, change from baseline)