Day 13: SDTM Validation with sdtmchecks

Running FDA Business Rules Against Your Domains

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1 Learning Objectives

By the end of Day 13, you will be able to:

1. Explain why SDTM validation is essential before creating ADaM datasets
2. Install and use the sdtmchecks package to run FDA business rules
3. Interpret validation reports and understand severity levels (ERROR vs WARNING)
4. Identify and fix common SDTM issues (missing variables, inconsistent dates, orphan records)
5. Implement a validation-first workflow - validate SDTM before proceeding to ADaM
6. Write custom validation checks for study-specific rules

---

2 Why Validate SDTM?

2.1 The Consequences of Bad SDTM Data

ImportantWhat Happens When SDTM Has Issues

1. FDA Refuse-to-File - The FDA can reject your entire submission if SDTM doesn’t conform
2. Reviewer queries - Every data issue generates a query that delays the review
3. ADaM errors cascade - ADaM is built on SDTM; bad SDTM = bad ADaM
4. Patient safety risk - Incorrect safety data could lead to wrong conclusions
5. Delayed approval - Each round of queries adds weeks/months to the timeline

Validation is not optional - it’s a survival skill.

2.2 What Gets Validated?

Check Category	What It Verifies	Example
Structural	Required variables present, correct types	Does DM have USUBJID?
Conformance	Values match CDISC Controlled Terminology	Is AESEV one of MILD/MODERATE/SEVERE?
Cross-domain	Consistency between domains	Are all AE subjects in DM?
Business rules	FDA-specific data requirements	Do all subjects have RFSTDTC populated?
Data quality	Logical consistency	Is AESTDTC ≤ AEENDTC?

---

3 Package Installation & Loading

if (!requireNamespace("dplyr", quietly = TRUE)) suppressMessages(install.packages("dplyr"))
if (!requireNamespace("pharmaversesdtm", quietly = TRUE)) suppressMessages(install.packages("pharmaversesdtm"))
if (!requireNamespace("lubridate", quietly = TRUE)) suppressMessages(install.packages("lubridate"))

# sdtmchecks is the key package for today
if (!requireNamespace("sdtmchecks", quietly = TRUE)) suppressMessages(install.packages("sdtmchecks"))

library(dplyr)
library(pharmaversesdtm)
library(sdtmchecks)
library(lubridate)

---

4 Understanding sdtmchecks

4.1 What is sdtmchecks?

sdtmchecks is a Pharmaverse package that implements FDA business rules and data quality checks against SDTM datasets. It was developed based on years of experience with FDA submissions and reviewer feedback.

4.2 Available Checks

# See what check functions are available
check_functions <- ls("package:sdtmchecks")

# Filter to just the check functions (they all start with "check_")
check_fns <- check_functions[grepl("^check_", check_functions)]

cat("Total check functions available:", length(check_fns), "\n\n")

Total check functions available: 109 

# Group by domain
cat("Checks by domain:\n")

Checks by domain:

domain_checks <- tibble(
  check = check_fns
) %>%
  mutate(
    domain = case_when(
      grepl("_ae_", check) ~ "AE",
      grepl("_dm_", check) ~ "DM",
      grepl("_ex_", check) ~ "EX",
      grepl("_lb_", check) ~ "LB",
      grepl("_vs_", check) ~ "VS",
      grepl("_ds_", check) ~ "DS",
      grepl("_cm_", check) ~ "CM",
      grepl("_mh_", check) ~ "MH",
      grepl("_eg_", check) ~ "EG",
      TRUE ~ "OTHER/MULTI"
    )
  ) %>%
  count(domain, name = "n_checks") %>%
  arrange(desc(n_checks))

print(domain_checks)

# A tibble: 10 × 2
   domain      n_checks
   <chr>          <int>
 1 OTHER/MULTI       35
 2 AE                28
 3 EX                13
 4 DS                 8
 5 DM                 7
 6 LB                 7
 7 CM                 5
 8 VS                 4
 9 EG                 1
10 MH                 1

# Show some specific check function names
cat("Sample AE checks:\n")

Sample AE checks:

check_fns[grepl("_ae_", check_fns)] %>% head(10) %>% cat(sep = "\n")

check_ae_aeacn_ds_disctx_covid
check_ae_aeacnoth
check_ae_aeacnoth_ds_disctx
check_ae_aeacnoth_ds_stddisc_covid
check_ae_aedecod
check_ae_aedthdtc_aesdth
check_ae_aedthdtc_ds_death
check_ae_aelat
check_ae_aeout
check_ae_aeout_aeendtc_aedthdtc

cat("\n\nSample DM checks:\n")

Sample DM checks:

check_fns[grepl("_dm_", check_fns)] %>% head(10) %>% cat(sep = "\n")

check_dm_actarm_arm
check_dm_ae_ds_death
check_dm_age_missing
check_dm_armcd
check_dm_dthfl_dthdtc
check_dm_usubjid_ae_usubjid
check_dm_usubjid_dup
check_sc_dm_eligcrit
check_sc_dm_seyeselc

TipNaming Convention

The check functions follow a consistent naming pattern:

check_<domain>_<what_is_checked>

For example: - check_ae_aestdtc_after_aeendtc - AE start date should not be after end date - check_dm_age_missing - Age should not be missing in DM - check_ae_aeser_aesdth - If AESER = “Y” and AESDTH = “Y”, consistency check

---

5 Loading Sample Data for Validation

# Load all SDTM domains from pharmaversesdtm
data("dm", package = "pharmaversesdtm")
data("ae", package = "pharmaversesdtm")
data("vs", package = "pharmaversesdtm")
data("lb", package = "pharmaversesdtm")
data("ex", package = "pharmaversesdtm")
data("ds", package = "pharmaversesdtm")

cat("Loaded SDTM domains:\n")

Loaded SDTM domains:

cat("  DM:", nrow(dm), "rows x", ncol(dm), "cols\n")

  DM: 306 rows x 26 cols

cat("  AE:", nrow(ae), "rows x", ncol(ae), "cols\n")

  AE: 1191 rows x 35 cols

cat("  VS:", nrow(vs), "rows x", ncol(vs), "cols\n")

  VS: 29643 rows x 24 cols

cat("  LB:", nrow(lb), "rows x", ncol(lb), "cols\n")

  LB: 59580 rows x 23 cols

cat("  EX:", nrow(ex), "rows x", ncol(ex), "cols\n")

  EX: 591 rows x 17 cols

cat("  DS:", nrow(ds), "rows x", ncol(ds), "cols\n")

  DS: 850 rows x 13 cols

---

6 Running Individual Checks

6.1 Check 1: AE Start Date After End Date

This is one of the most basic but important checks - an AE cannot start after it ends!

# Run the AE date check with error handling
# Note: Some versions of sdtmchecks have issues with NA handling in date fields
result_ae_dates <- tryCatch({
  check_ae_aestdtc_after_aeendtc(AE = ae)
}, error = function(e) {
  # If the check function errors, perform a manual check
  cat("Note: Using manual check due to package compatibility issue\n")
  ae %>%
    filter(!is.na(AESTDTC), !is.na(AEENDTC)) %>%
    mutate(
      ae_start = ymd_hms(AESTDTC, truncated = 3),
      ae_end = ymd_hms(AEENDTC, truncated = 3)
    ) %>%
    filter(ae_start > ae_end) %>%
    select(USUBJID, AESEQ, AEDECOD, AESTDTC, AEENDTC)
})

Note: Using manual check due to package compatibility issue

cat("Check: AE start date after end date\n")

Check: AE start date after end date

cat("Result type:", class(result_ae_dates), "\n\n")

Result type: tbl_df tbl data.frame 

if (is.data.frame(result_ae_dates) && nrow(result_ae_dates) > 0) {
  cat("Issues found:", nrow(result_ae_dates), "\n")
  print(head(result_ae_dates))
} else {
  cat("No issues found - all AE start dates are on or before end dates ✓\n")
}

No issues found - all AE start dates are on or before end dates ✓

6.2 Check 2: AE Missing AEDECOD

The decoded term (MedDRA preferred term) should always be populated:

result_ae_decod <- tryCatch({
  check_ae_aedecod(AE = ae)
}, error = function(e) {
  # Manual check if package function fails
  cat("Note: Using manual check due to package compatibility issue\n")
  ae %>%
    filter(is.na(AEDECOD) | AEDECOD == "") %>%
    select(USUBJID, AESEQ, AETERM, AEDECOD)
})

cat("Check: AE missing AEDECOD\n")

Check: AE missing AEDECOD

if (is.data.frame(result_ae_decod) && nrow(result_ae_decod) > 0) {
  cat("Issues found:", nrow(result_ae_decod), "\n")
  print(head(result_ae_decod))
} else {
  cat("No issues found - all AEs have AEDECOD populated ✓\n")
}

No issues found - all AEs have AEDECOD populated ✓

6.3 Check 3: DM Missing Age

result_dm_age <- tryCatch({
  check_dm_age_missing(DM = dm)
}, error = function(e) {
  # Manual check if package function fails
  cat("Note: Using manual check due to package compatibility issue\n")
  dm %>%
    filter(is.na(AGE)) %>%
    select(USUBJID, AGE, SEX, RACE)
})

cat("Check: DM missing age\n")

Check: DM missing age

if (is.data.frame(result_dm_age) && nrow(result_dm_age) > 0) {
  cat("Issues found:", nrow(result_dm_age), "\n")
  print(head(result_dm_age))
} else {
  cat("No issues found - all subjects have age populated ✓\n")
}

No issues found - all subjects have age populated ✓

---

7 Running Multiple Checks and Building a Report

7.1 Cross-Domain Checks

These checks compare data across domains to ensure consistency:

# Check: AE action taken
result_ae_dm <- tryCatch({
  check_ae_aeacn(AE = ae, DS = ds)
}, error = function(e) {
  cat("Note: Check skipped due to package compatibility issue\n")
  data.frame()
})

Note: Check skipped due to package compatibility issue

cat("Check: AE action taken\n")

Check: AE action taken

if (is.data.frame(result_ae_dm) && nrow(result_ae_dm) > 0) {
  cat("Issues found:", nrow(result_ae_dm), "\n")
  print(head(result_ae_dm, 5))
} else {
  cat("No issues found ✓\n")
}

No issues found ✓

7.2 Building a Comprehensive Validation Report

# Run a batch of checks and compile results
run_check <- function(check_name, check_fn, ...) {
  tryCatch({
    result <- check_fn(...)
    if (is.data.frame(result) && nrow(result) > 0) {
      tibble(
        CHECK = check_name,
        STATUS = "FINDING",
        N_ISSUES = nrow(result),
        DETAILS = paste(names(result), collapse = ", ")
      )
    } else {
      tibble(
        CHECK = check_name,
        STATUS = "PASS",
        N_ISSUES = 0L,
        DETAILS = "No issues"
      )
    }
  }, error = function(e) {
    tibble(
      CHECK = check_name,
      STATUS = "ERROR",
      N_ISSUES = NA_integer_,
      DETAILS = conditionMessage(e)
    )
  })
}

# Run a selection of important checks
cat("=== SDTM VALIDATION REPORT ===\n\n")

=== SDTM VALIDATION REPORT ===

validation_results <- bind_rows(
  run_check("AE: Start date after end date",
            check_ae_aestdtc_after_aeendtc, AE = ae),
  run_check("AE: Missing AEDECOD",
            check_ae_aedecod, AE = ae),
  run_check("DM: Missing age",
            check_dm_age_missing, DM = dm),
  run_check("AE: Action taken check",
            check_ae_aeacn, AE = ae, DS = ds),
  run_check("AE: AE term consistency",
            check_ae_aeterm, AE = ae)
)

print(validation_results)

# A tibble: 5 × 4
  CHECK                         STATUS N_ISSUES DETAILS                         
  <chr>                         <chr>     <int> <chr>                           
1 AE: Start date after end date ERROR        NA NAs are not allowed in subscrip…
2 AE: Missing AEDECOD           PASS          0 No issues                       
3 DM: Missing age               PASS          0 No issues                       
4 AE: Action taken check        ERROR        NA object 'check_ae_aeacn' not fou…
5 AE: AE term consistency       ERROR        NA object 'check_ae_aeterm' not fo…

# Summary statistics
cat("\n=== VALIDATION SUMMARY ===\n")

=== VALIDATION SUMMARY ===

cat("Total checks run:", nrow(validation_results), "\n")

Total checks run: 5 

cat("Checks passed:", sum(validation_results$STATUS == "PASS"), "\n")

Checks passed: 2 

cat("Checks with findings:", sum(validation_results$STATUS == "FINDING"), "\n")

Checks with findings: 0 

cat("Checks with errors:", sum(validation_results$STATUS == "ERROR"), "\n")

Checks with errors: 3 

---

8 Common SDTM Issues and How to Fix Them

8.1 Issue 1: Missing Required Variables

# Check if all required variables are present
check_required_vars <- function(domain_data, domain_name, required_vars) {
  present <- required_vars %in% names(domain_data)
  
  results <- tibble(
    Domain = domain_name,
    Variable = required_vars,
    Present = present,
    Status = if_else(present, "✓", "✗ MISSING")
  )
  
  return(results)
}

# Check DM required variables
dm_required <- c("STUDYID", "USUBJID", "DOMAIN", "SUBJID", "SITEID",
                 "SEX", "AGE", "AGEU", "RACE", "ARM", "ARMCD",
                 "RFSTDTC", "RFENDTC", "COUNTRY")

cat("DM Required Variables Check:\n")

DM Required Variables Check:

check_required_vars(dm, "DM", dm_required) %>% print()

# A tibble: 14 × 4
   Domain Variable Present Status
   <chr>  <chr>    <lgl>   <chr> 
 1 DM     STUDYID  TRUE    ✓     
 2 DM     USUBJID  TRUE    ✓     
 3 DM     DOMAIN   TRUE    ✓     
 4 DM     SUBJID   TRUE    ✓     
 5 DM     SITEID   TRUE    ✓     
 6 DM     SEX      TRUE    ✓     
 7 DM     AGE      TRUE    ✓     
 8 DM     AGEU     TRUE    ✓     
 9 DM     RACE     TRUE    ✓     
10 DM     ARM      TRUE    ✓     
11 DM     ARMCD    TRUE    ✓     
12 DM     RFSTDTC  TRUE    ✓     
13 DM     RFENDTC  TRUE    ✓     
14 DM     COUNTRY  TRUE    ✓     

8.2 Issue 2: Orphan Records (Records Without a DM Entry)

# Check for AE subjects not in DM
ae_orphans <- ae %>%
  anti_join(dm, by = "USUBJID")

cat("\nOrphan Record Check:\n")

Orphan Record Check:

cat("AE subjects not in DM:", n_distinct(ae_orphans$USUBJID), "\n")

AE subjects not in DM: 0 

# Check for EX subjects not in DM
ex_orphans <- ex %>%
  anti_join(dm, by = "USUBJID")
cat("EX subjects not in DM:", n_distinct(ex_orphans$USUBJID), "\n")

EX subjects not in DM: 0 

if (nrow(ae_orphans) > 0) {
  cat("\nOrphan AE subjects:\n")
  print(distinct(ae_orphans, USUBJID))
}

8.3 Issue 3: Date Consistency

# Check: AE start dates should be on or after reference start date
ae_date_check <- ae %>%
  left_join(dm %>% select(USUBJID, RFSTDTC), by = "USUBJID") %>%
  filter(!is.na(AESTDTC), !is.na(RFSTDTC)) %>%
  mutate(
    ae_start = ymd(AESTDTC),
    ref_start = ymd(RFSTDTC),
    BEFORE_TREATMENT = ae_start < ref_start
  )

n_before <- sum(ae_date_check$BEFORE_TREATMENT, na.rm = TRUE)
cat("\nDate Consistency Check:\n")

Date Consistency Check:

cat("AEs starting before first dose date:", n_before, "\n")

AEs starting before first dose date: 45 

if (n_before > 0) {
  cat("(These may be pre-treatment AEs - verify they are expected)\n")
  ae_date_check %>%
    filter(BEFORE_TREATMENT) %>%
    select(USUBJID, AEDECOD, AESTDTC, RFSTDTC) %>%
    head(5) %>%
    print()
}

(These may be pre-treatment AEs - verify they are expected)
# A tibble: 5 × 4
  USUBJID     AEDECOD             AESTDTC    RFSTDTC   
  <chr>       <chr>               <chr>      <chr>     
1 01-701-1111 ERYTHEMA            2012-09-02 2012-09-07
2 01-701-1111 ERYTHEMA            2012-09-02 2012-09-07
3 01-701-1111 LOCALISED INFECTION 2012-07-08 2012-09-07
4 01-701-1111 PRURITUS            2012-09-02 2012-09-07
5 01-701-1111 PRURITUS            2012-09-02 2012-09-07

8.4 Issue 4: Controlled Terminology Violations

# Check AE severity against allowed values
allowed_aesev <- c("MILD", "MODERATE", "SEVERE")

ct_violations <- ae %>%
  filter(!is.na(AESEV)) %>%
  filter(!(AESEV %in% allowed_aesev))

cat("\nControlled Terminology Check (AESEV):\n")

Controlled Terminology Check (AESEV):

if (nrow(ct_violations) > 0) {
  cat("Invalid AESEV values found:", nrow(ct_violations), "\n")
  ct_violations %>% count(AESEV) %>% print()
} else {
  cat("All AESEV values are valid ✓\n")
  cat("Valid values:", paste(allowed_aesev, collapse = ", "), "\n")
}

All AESEV values are valid ✓
Valid values: MILD, MODERATE, SEVERE 

# Check AESER
allowed_aeser <- c("Y", "N")
aeser_violations <- ae %>%
  filter(!is.na(AESER)) %>%
  filter(!(AESER %in% allowed_aeser))

cat("\nControlled Terminology Check (AESER):\n")

Controlled Terminology Check (AESER):

if (nrow(aeser_violations) > 0) {
  cat("Invalid AESER values found:", nrow(aeser_violations), "\n")
} else {
  cat("All AESER values are valid ✓\n")
}

All AESER values are valid ✓

---

9 Writing Custom Validation Checks

Sometimes you need study-specific validation rules. Here’s how to write your own:

# ---- Custom Check Function Template ----
check_custom_ae_duration <- function(AE, max_duration = 365) {
  # Purpose: Flag AEs with unreasonably long durations
  
  ae_with_dur <- AE %>%
    filter(!is.na(AESTDTC), !is.na(AEENDTC)) %>%
    mutate(
      DURATION = as.numeric(ymd(AEENDTC) - ymd(AESTDTC))
    ) %>%
    filter(DURATION > max_duration)
  
  if (nrow(ae_with_dur) > 0) {
    ae_with_dur %>%
      select(USUBJID, AESEQ, AEDECOD, AESTDTC, AEENDTC, DURATION) %>%
      mutate(MESSAGE = paste0("AE duration of ", DURATION, 
                              " days exceeds ", max_duration, " day threshold"))
  } else {
    data.frame()  # No issues
  }
}

# Run our custom check
result_duration <- check_custom_ae_duration(ae, max_duration = 180)

cat("Custom Check: AE Duration > 180 days\n")

Custom Check: AE Duration > 180 days

if (nrow(result_duration) > 0) {
  cat("Issues found:", nrow(result_duration), "\n")
  print(head(result_duration, 5))
} else {
  cat("No issues found ✓\n")
}

Issues found: 6 
# A tibble: 5 × 7
  USUBJID     AESEQ AEDECOD           AESTDTC    AEENDTC    DURATION MESSAGE    
  <chr>       <dbl> <chr>             <chr>      <chr>         <dbl> <chr>      
1 01-703-1100     6 OEDEMA PERIPHERAL 2013-02-28 2013-09-14      198 AE duratio…
2 01-703-1100     8 OEDEMA PERIPHERAL 2013-02-28 2013-09-14      198 AE duratio…
3 01-705-1393     2 PRURITUS          2011-12-05 2013-02-20      443 AE duratio…
4 01-705-1393     4 PRURITUS          2011-12-05 2013-02-20      443 AE duratio…
5 01-706-1041     4 IRRITABILITY      2014-01-15 2014-07-29      195 AE duratio…

# ---- Custom Check: Duplicate Records ----
check_custom_duplicates <- function(data, domain, key_vars) {
  dupes <- data %>%
    group_by(across(all_of(key_vars))) %>%
    filter(n() > 1) %>%
    ungroup()
  
  if (nrow(dupes) > 0) {
    cat(domain, "- Duplicate records found:", nrow(dupes), "\n")
    dupes %>%
      select(all_of(key_vars)) %>%
      head(10)
  } else {
    cat(domain, "- No duplicates ✓\n")
    data.frame()
  }
}

# Check for duplicate AE records
cat("Duplicate Record Checks:\n")

Duplicate Record Checks:

check_custom_duplicates(ae, "AE", c("USUBJID", "AESEQ"))

AE - No duplicates ✓

data frame with 0 columns and 0 rows

check_custom_duplicates(dm, "DM", c("USUBJID"))

DM - No duplicates ✓

data frame with 0 columns and 0 rows

---

10 Validation Workflow: Putting It All Together

cat("=== RECOMMENDED VALIDATION WORKFLOW ===\n\n")

=== RECOMMENDED VALIDATION WORKFLOW ===

workflow <- tibble::tribble(
  ~Step, ~Action,                                    ~Tool,
  1L,    "Check required variables present",          "Custom + define.xml",
  2L,    "Run sdtmchecks basic checks",               "sdtmchecks",
  3L,    "Run cross-domain consistency checks",       "sdtmchecks",
  4L,    "Check controlled terminology",              "Custom + CT package",
  5L,    "Run study-specific custom checks",          "Custom functions",
  6L,    "Review and categorize findings",            "Manual review",
  7L,    "Fix critical issues, document acceptable deviations",  "Code fixes + documentation",
  8L,    "Re-run validation to confirm fixes",        "sdtmchecks",
  9L,    "Generate final validation report",          "Markdown/HTML report",
  10L,   "Proceed to ADaM creation",                  "admiral + metacore"
)

print(workflow)

# A tibble: 10 × 3
    Step Action                                              Tool               
   <int> <chr>                                               <chr>              
 1     1 Check required variables present                    Custom + define.xml
 2     2 Run sdtmchecks basic checks                         sdtmchecks         
 3     3 Run cross-domain consistency checks                 sdtmchecks         
 4     4 Check controlled terminology                        Custom + CT package
 5     5 Run study-specific custom checks                    Custom functions   
 6     6 Review and categorize findings                      Manual review      
 7     7 Fix critical issues, document acceptable deviations Code fixes + docum…
 8     8 Re-run validation to confirm fixes                  sdtmchecks         
 9     9 Generate final validation report                    Markdown/HTML repo…
10    10 Proceed to ADaM creation                            admiral + metacore 

NoteValidation in Practice

In production environments, validation checks are typically:

• Automated - Run as part of a CI/CD pipeline
• Tiered - ERROR (must fix), WARNING (should fix), INFO (review)
• Documented - Each finding gets a resolution or justification
• Versioned - Check results are saved with timestamps
• Reviewed - A second programmer reviews the findings

Some organizations run Pinnacle 21 (OpenCDISC) in addition to sdtmchecks for a more comprehensive validation.

---

11 Deliverable Summary

Today you completed the following:

Task	Status
Understood why SDTM validation is essential	✓ Done
Explored the sdtmchecks package and available checks	✓ Done
Ran individual validation checks (AE dates, DM age, etc.)	✓ Done
Built a comprehensive validation report	✓ Done
Identified and analyzed common SDTM issues	✓ Done
Wrote custom validation checks	✓ Done
Learned the validation workflow	✓ Done

---

12 Key Takeaways

1. Validate before ADaM - Never build ADaM on unvalidated SDTM data
2. sdtmchecks implements FDA rules - These checks are based on real submission experience
3. Cross-domain checks are critical - Orphan records and date inconsistencies are common
4. CT compliance is mandatory - Only CDISC-approved values are acceptable
5. Custom checks add value - Study-specific rules complement package checks
6. Document everything - Every finding needs a resolution or justification

---

13 Resources

• sdtmchecks Documentation - Official package documentation
• sdtmchecks GitHub - Source code and check list
• Pinnacle 21 - Commercial CDISC validation tool
• CDISC Conformance Rules - Official conformance rules
• FDA Data Standards Catalog - FDA data standards requirements

---

14 What’s Next?

In Day 14, we will complete Week 2 with the Week 2 Capstone: Metadata-Driven SDTM:

• Using metacore to load and work with specification objects
• Applying metadata labels, types, and formats with metatools
• Exporting submission-ready .xpt files with xportr
• End-to-end pipeline: raw data → SDTM → validate → export
• Comprehensive review of all SDTM concepts before entering ADaM in Week 3

---