# CDISC Clinical Data Standards Clinical trial data follows strict organizational standards defined by CDISC (Clinical Data Interchange Standards Consortium). These standards specify exactly which variables must appear in each dataset, what values are permitted, how dates should be formatted, and how analysis datasets trace back to their source observations. Regulatory agencies like the FDA and PMDA require CDISC-compliant data for drug submissions, making adherence to these standards mandatory for pharmaceutical organizations. Pointblank provides native support for the three major CDISC data models: **SDTM** (Study Data Tabulation Model) for raw collected data, **ADaM** (Analysis Data Model) for analysis-ready datasets, and **Define-XML** for the metadata documents that describe both. Whether you are preparing a regulatory submission, running quality checks on incoming CRO data, or building automated validation pipelines for clinical data warehouses, Pointblank can generate the appropriate checks directly from the standard specifications. # Prerequisites CDISC XML parsing (Define-XML and Controlled Terminology files) requires the `lxml` library: ``` bash pip install lxml ``` Or install Pointblank with the CDISC extra: ``` bash pip install pointblank[cdisc] ``` Reading metadata from SAS Transport (`.xpt`) files (used in the *Exporting Metadata* example below) additionally requires the `pyreadstat` library: ``` bash pip install pyreadstat ``` The SDTM and ADaM domain templates are built into Pointblank and require no additional dependencies. They encode the structural requirements from the SDTM Implementation Guide 3.4 and the ADaM Implementation Guide 1.1 directly in Python, so you can validate clinical datasets without needing the original XML specification documents. # Define-XML Import Define-XML is the CDISC standard for documenting dataset structure. It describes every variable in a submission package: its name, label, data type, length, origin, and associated controlled terminology. Pointblank can parse Define-XML 2.0 and 2.1 documents and extract this metadata into a form suitable for validation. ## Importing a Define-XML File The [import_metadata()](../../reference/import_metadata.md#pointblank.import_metadata) function with `format="cdisc_define"` reads a Define-XML file and returns a [MetadataPackage](../../reference/MetadataPackage.md#pointblank.MetadataPackage) containing metadata for all datasets defined in the document. So that you can run these examples without supplying your own files, Pointblank bundles a small set of sample metadata documents that you can locate with `load_metadata_example()`: ``` python import pointblank as pb # Locate the bundled Define-XML example and import all of its datasets define_path = pb.load_metadata_example("define.xml") package = pb.import_metadata(define_path, format="cdisc_define") # List the datasets defined in the document for name in package.keys(): meta = package[name] print(f"{name}: {meta.dataset_label} ({len(meta.variables)} variables)") ``` DM: Demographics (13 variables) AE: Adverse Events (10 variables) Each dataset in the package is a [MetadataImport](../../reference/MetadataImport.md#pointblank.MetadataImport) object with full variable-level metadata. You can access individual datasets by name and generate validation from them. Here we validate a small Demographics table against the metadata extracted from the Define-XML: ``` python import polars as pl # Get metadata for the Demographics domain dm_meta = package["DM"] # A small Demographics dataset to validate dm_data = pl.DataFrame({ "STUDYID": ["XYZ789"] * 4, "DOMAIN": ["DM"] * 4, "USUBJID": ["XYZ789-001", "XYZ789-002", "XYZ789-003", "XYZ789-004"], "SUBJID": ["001", "002", "003", "004"], "RFSTDTC": ["2024-01-15", "2024-01-20", "2024-02-01", "2024-02-10"], "RFENDTC": ["2024-06-15", "2024-06-20", "2024-07-01", "2024-07-10"], "SITEID": ["SITE01", "SITE01", "SITE02", "SITE02"], "AGE": [45, 62, 38, 55], "AGEU": ["YEARS"] * 4, "SEX": ["M", "F", "M", "F"], "RACE": ["WHITE", "BLACK OR AFRICAN AMERICAN", "ASIAN", "WHITE"], "ARMCD": ["DRUG", "PLACEBO", "DRUG", "PLACEBO"], "ARM": ["Active Drug 10mg", "Placebo", "Active Drug 10mg", "Placebo"], }) # Generate validation for the Demographics data from the Define-XML metadata validation = dm_meta.to_validate(data=dm_data).interrogate() validation ``` F0ZSgtMTgwLjAwMDAwMCkgdHJhbnNsYXRlKC0xNS45MjkyMzAsIC0xMS44OTQ3MzcpICIgLz4KICAgICAgICA8L2c+CiAgICA8L2c+Cjwvc3ZnPg==" /> MS44OTQ3MzcpIHJvdGF0ZSgtMTgwLjAwMDAwMCkgdHJhbnNsYXRlKC0xNS45MjkyMzAsIC0xMS44OTQ3MzcpICIgLz4KICAgICAgICA8L2c+CiAgICA8L2c+Cjwvc3ZnPg==" />
Pointblank Validation
Validation: DM (cdisc_define) Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_schema_match()
-- SCHEMA ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
col_vals_not_null()
STUDYID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 3
col_vals_not_null()
DOMAIN -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_vals_not_null()
USUBJID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 5
col_vals_not_null()
SUBJID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 6
col_vals_in_set()
SEX M, F, U ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 7
col_vals_in_set()
RACE WHITE, BLACK OR AFRICAN AMERICAN, ASIAN, AMERICAN INDIAN OR ALASKA NATIVE, NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER ✓ 4 4
1.00		 0
0.00		 -- -- -- --
Notes

Step 1 (schema_check) ✓ Schema validation passed.

Schema Comparison
TARGET EXPECTED
COLUMN DATA TYPE COLUMN DATA TYPE
1 STUDYID String 1 STUDYID ✓ String ✓
2 DOMAIN String 2 DOMAIN ✓ String ✓
3 USUBJID String 3 USUBJID ✓ String ✓
4 SUBJID String 4 SUBJID ✓ String ✓
5 RFSTDTC String 5 RFSTDTC ✓ String ✓
6 RFENDTC String 6 RFENDTC ✓ String ✓
7 SITEID String 7 SITEID ✓ String ✓
8 AGE Int64 8 AGE ✓ Int64 ✓
9 AGEU String 9 AGEU ✓ String ✓
10 SEX String 10 SEX ✓ String ✓
11 RACE String 11 RACE ✓ String ✓
12 ARMCD String 12 ARMCD ✓ String ✓
13 ARM String 13 ARM ✓ String ✓
Supplied Column Schema: [('STUDYID', 'String'), ('DOMAIN', 'String'), ('USUBJID', 'String'), ('SUBJID', 'String'), ('RFSTDTC', 'String'), ('RFENDTC', 'String'), ('SITEID', 'String'), ('AGE', 'Int64'), ('AGEU', 'String'), ('SEX', 'String'), ('RACE', 'String'), ('ARMCD', 'String'), ('ARM', 'String')]
Schema Match Settings COMPLETE IN ORDER COLUMN ≠ column DTYPE ≠ dtype float ≠ float64
## What Gets Extracted Define-XML documents contain rich structural metadata. Pointblank extracts the following elements: | Define-XML Element | Pointblank Mapping | |----|----| | ItemGroupDef (dataset) | [MetadataImport](../../reference/MetadataImport.md#pointblank.MetadataImport) per dataset | | ItemDef (variable) | [VariableMetadata](../../reference/VariableMetadata.md#pointblank.VariableMetadata) with name, label, dtype | | DataType attribute | Mapped to Pointblank dtype (String, Int64, Float64, etc.) | | Length attribute | `max_length` constraint on [VariableMetadata](../../reference/VariableMetadata.md#pointblank.VariableMetadata) | | SignificantDigits | `significant_digits` on [VariableMetadata](../../reference/VariableMetadata.md#pointblank.VariableMetadata) | | Origin (CRF, Derived, etc.) | `origin` field | | CodeListRef | `codelist_ref` linking to the associated codelist | | ComputationalMethod | `computational_method` for derived variables | | Role/RoleCodeListOID | `cdisc_role` (Identifier, Topic, etc.) | | CodeList | [Codelist](../../reference/Codelist.md#pointblank.Codelist) object with all permitted values | | Mandatory="Yes" | `required=True` on [VariableMetadata](../../reference/VariableMetadata.md#pointblank.VariableMetadata) | ## Controlled Terminology from Define-XML Define-XML documents embed the codelists that constrain variable values. When Pointblank parses a Define-XML, all codelists are extracted and linked to their respective variables. The [to_validate()](../../reference/Contract.md#pointblank.Contract.to_validate) method then generates [col_vals_in_set()](../../reference/Validate.col_vals_in_set.md#pointblank.Validate.col_vals_in_set) checks for each variable that references a codelist: ``` python # Inspect codelists referenced by the Demographics domain for cl_name, codelist in dm_meta.codelists.items(): print(f"{cl_name}: {codelist.to_set()[:5]}...") # first 5 values print(f" Extensible: {codelist.extensible}") ``` Sex: ['M', 'F', 'U']... Extensible: False Race: ['WHITE', 'BLACK OR AFRICAN AMERICAN', 'ASIAN', 'AMERICAN INDIAN OR ALASKA NATIVE', 'NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER']... Extensible: False The [extensible](../../reference/Codelist.md#pointblank.Codelist.extensible) flag records an important distinction. Non-extensible codelists require strict adherence: any value not in the codelist is a conformance issue. Extensible codelists permit sponsor-defined additions, so a value outside the published set is not necessarily an error. Pointblank currently generates the same [col_vals_in_set()](../../reference/Validate.col_vals_in_set.md#pointblank.Validate.col_vals_in_set) check for both kinds of codelist. For extensible codelists you will typically want to relax that step to reflect that additions are allowed, for example by attaching a warning-level threshold to the step rather than treating every out-of-set value as a hard failure. # CDISC Controlled Terminology Import Beyond the codelists embedded in Define-XML, CDISC publishes standalone Controlled Terminology packages as XML files. These contain the canonical value sets for concepts like SEX, RACE, ROUTE OF ADMINISTRATION, and hundreds of others. Pointblank can parse these directly. Importing a CT package returns a [MetadataPackage](../../reference/MetadataPackage.md#pointblank.MetadataPackage) whose entries are keyed by codelist name. Each entry is a [MetadataImport](../../reference/MetadataImport.md#pointblank.MetadataImport) that holds the codelist itself: ``` python # Import the bundled CDISC CT example ct_path = pb.load_metadata_example("sdtm_ct.xml") ct = pb.import_metadata(ct_path, format="cdisc_ct") # List the codelists in the package print(list(ct.keys())) # Access a codelist by name sex_codelist = ct["Sex"].codelists["Sex"] print(f"SEX values: {sex_codelist.to_set()}") print(f"Extensible: {sex_codelist.extensible}") ``` ['Sex', 'Severity/Intensity Scale for Adverse Events', 'No Yes Response', 'Race', 'Route of Administration'] SEX values: ['F', 'M', 'U', 'UNDIFFERENTIATED'] Extensible: False Once you have a codelist, its permitted values feed directly into a [col_vals_in_set()](../../reference/Validate.col_vals_in_set.md#pointblank.Validate.col_vals_in_set) check: ``` python # Use the CT-derived value set in a validation validation = ( pb.Validate(data=dm_data) .col_vals_in_set(columns="SEX", set=sex_codelist.to_set()) .interrogate() ) validation ```
Pointblank Validation
2026-07-13|17:59:04 Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_in_set()
SEX F, M, U, UNDIFFERENTIATED ✓ 4 4
1.00		 0
0.00		 -- -- -- --
Controlled Terminology packages version quarterly (e.g., 2024-03-29, 2024-06-28). Referencing a specific version ensures reproducible validation results. In production pipelines, you would pin the CT version to match what was specified in your study's Define-XML. # SDTM Domain Templates The Study Data Tabulation Model organizes clinical trial data into domains: Demographics (DM), Adverse Events (AE), Laboratory Results (LB), Vital Signs (VS), and many others. Each domain has a defined set of required and expected variables, with specific roles, types, and length constraints. Pointblank includes built-in templates for eight commonly used SDTM domains. These templates encode the structural requirements from the SDTM Implementation Guide 3.4 directly, so you can validate data against the standard without needing a Define-XML file. ## Available Domains ``` python import pointblank as pb from pointblank.metadata import list_sdtm_domains, get_sdtm_domain # List all available SDTM domain templates domains = list_sdtm_domains() for d in domains: template = get_sdtm_domain(d) req_count = sum(1 for v in template.variables if v.required) print(f" {d}: {template.label} ({req_count} required, {len(template.variables)} total vars)") ``` AE: Adverse Events (6 required, 28 total vars) CM: Concomitant Medications (5 required, 17 total vars) DM: Demographics (9 required, 26 total vars) DS: Disposition (6 required, 11 total vars) EX: Exposure (5 required, 15 total vars) LB: Laboratory Test Results (6 required, 26 total vars) MH: Medical History (5 required, 12 total vars) VS: Vital Signs (6 required, 19 total vars) Each template provides the full variable specification for its domain, including which variables are required (core="Req"), expected (core="Exp"), or permissible (core="Perm") per the Implementation Guide. ## Inspecting a Domain Template You can examine the variable specifications for any domain to understand what Pointblank will check: ``` python # Get the Demographics domain template dm = get_sdtm_domain("DM") print(f"Domain: {dm.domain} - {dm.label}") print(f"Class: {dm.domain_class}") print(f"Repeating: {dm.repeating}") print() # Show required variables print("Required variables (core='Req'):") for var in dm.variables: if var.required: ct_info = f" [CT: {var.controlled_term}]" if var.controlled_term else "" print(f" {var.name:12s} {var.dtype:4s} {var.role:12s} {var.label}{ct_info}") ``` Domain: DM - Demographics Class: Special Purpose Repeating: False Required variables (core='Req'): STUDYID Char Identifier Study Identifier DOMAIN Char Identifier Domain Abbreviation USUBJID Char Identifier Unique Subject Identifier SUBJID Char Topic Subject Identifier for the Study SITEID Char Qualifier Study Site Identifier SEX Char Qualifier Sex [CT: SEX] ARMCD Char Qualifier Planned Arm Code ARM Char Qualifier Description of Planned Arm COUNTRY Char Qualifier Country [CT: COUNTRY] ## Structural Validation The [validate_sdtm_structure()](../../reference/validate_sdtm_structure.md#pointblank.validate_sdtm_structure) function performs a quick check that a dataset contains all required variables for its domain. This is useful as a fast pre-check before running the full validation workflow: ``` python import polars as pl from pointblank.metadata import validate_sdtm_structure # A minimal Demographics dataset dm_data = pl.DataFrame({ "STUDYID": ["STUDY01"] * 4, "DOMAIN": ["DM"] * 4, "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003", "STUDY01-004"], "SUBJID": ["001", "002", "003", "004"], "RFSTDTC": ["2024-01-15", "2024-01-20", "2024-02-01", "2024-02-10"], "RFENDTC": ["2024-06-15", "2024-06-20", "2024-07-01", "2024-07-10"], "SITEID": ["SITE01", "SITE01", "SITE02", "SITE02"], "AGE": [45, 62, 38, 55], "AGEU": ["YEARS"] * 4, "SEX": ["M", "F", "M", "F"], "RACE": ["WHITE", "BLACK OR AFRICAN AMERICAN", "ASIAN", "WHITE"], "ARMCD": ["DRUG", "PLACEBO", "DRUG", "PLACEBO"], "ARM": ["Active Drug 10mg", "Placebo", "Active Drug 10mg", "Placebo"], "COUNTRY": ["USA", "USA", "GBR", "GBR"], }) result = validate_sdtm_structure(dm_data, domain="DM") print(f"Valid: {result['valid']}") if result["missing_required"]: print(f"Missing required: {result['missing_required']}") if result["unknown_variables"]: print(f"Unknown variables: {result['unknown_variables'][:5]}") ``` Valid: True ## Full SDTM Validation The [validate_sdtm()](../../reference/validate_sdtm.md#pointblank.validate_sdtm) function generates a comprehensive validation workflow that checks far more than just structure. It produces a [Validate](../../reference/Validate.md#pointblank.Validate) object with checks for required variable non-nullness, DOMAIN value correctness, sequence number positivity, string length constraints, and ISO 8601 date formatting: ``` python from pointblank.metadata import validate_sdtm # Generate and run the full SDTM DM validation validation = validate_sdtm(data=dm_data, domain="DM").interrogate() validation ``` 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" />
Pointblank Validation
SDTM DM Validation Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_not_null()
STUDYID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
col_vals_not_null()
DOMAIN -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 3
col_vals_not_null()
USUBJID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_vals_not_null()
SUBJID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 5
col_vals_not_null()
SITEID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 6
col_vals_not_null()
SEX -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 7
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" /> col_vals_not_null()
ARMCD -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 8
col_vals_not_null()
ARM -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 9
col_vals_not_null()
COUNTRY -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 10
col_vals_in_set()
DOMAIN DM ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 11
col_vals_expr()

STUDYID length <= 20

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 12
col_vals_expr()

DOMAIN length <= 2

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 13
col_vals_expr()

USUBJID length <= 40

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 14
col_vals_expr()

SUBJID length <= 20

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 15
col_vals_expr()

RFSTDTC length <= 64

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 16
col_vals_expr()

RFENDTC length <= 64

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 17
col_vals_expr()

SITEID length <= 20

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 18
col_vals_expr()

AGEU length <= 10

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 19
col_vals_expr()

SEX length <= 2

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 20
col_vals_expr()

RACE length <= 60

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 21
col_vals_expr()

ARMCD length <= 20

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 22
col_vals_expr()

ARM length <= 200

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 23
col_vals_expr()

COUNTRY length <= 3

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 24
col_vals_regex()
RFSTDTC ^(\d{4})(-\d{2}(-\d{2}(T\d{2}(:\d{2}(:\d{2})?)?)?)?)?$ ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 25
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" /> col_vals_regex()
RFENDTC ^(\d{4})(-\d{2}(-\d{2}(T\d{2}(:\d{2}(:\d{2})?)?)?)?)?$ ✓ 4 4
1.00		 0
0.00		 -- -- -- --
The validation checks the following rules automatically: | Check | Description | |----|----| | Required variables non-null | Every variable with core="Req" must have no nulls | | DOMAIN value | The DOMAIN column must contain only the expected domain code | | Sequence numbers | `--SEQ` variables must be positive integers | | String lengths | Character variables must not exceed their defined max length | | ISO 8601 dates | All `--DTC` timing variables must match the CDISC date pattern | ## ISO 8601 Date Validation CDISC uses a specific subset of ISO 8601 that allows partial dates. A date might be fully specified as `2024-03-15T10:30:00` or partially specified as just `2024-03` (year and month known, day unknown). The validation checks that all timing variables (`--DTC` columns like `RFSTDTC`, `AESTDTC`, `LBDTC`) conform to this pattern: Valid: 2024-03-15T10:30:00 (full datetime) Valid: 2024-03-15 (date only) Valid: 2024-03 (year-month only) Valid: 2024 (year only) Invalid: 03/15/2024 (wrong format) Invalid: 15-Mar-2024 (wrong format) This catches a common data quality issue where dates are entered in locale-specific formats rather than the required ISO 8601 pattern. ## Catching Conformance Problems The examples so far have used clean data, so every check passes. The point of validation, of course, is to surface problems. Here is the same DM domain with three deliberate defects: a missing `USUBJID` (a required variable), a row where `DOMAIN` is `"AE"` instead of `"DM"`, and a `RFSTDTC` value entered in the wrong date format: ``` python # A Demographics dataset with three injected conformance issues dm_data_with_issues = pl.DataFrame({ "STUDYID": ["STUDY01"] * 4, "DOMAIN": ["DM", "DM", "AE", "DM"], # row 3: wrong domain code "USUBJID": ["STUDY01-001", "STUDY01-002", None, "STUDY01-004"], # row 3: required but null "SUBJID": ["001", "002", "003", "004"], "RFSTDTC": ["2024-01-15", "03/15/2024", "2024-02-01", "2024-02-10"], # row 2: not ISO 8601 "SITEID": ["SITE01", "SITE01", "SITE02", "SITE02"], "AGE": [45, 62, 38, 55], "AGEU": ["YEARS"] * 4, "SEX": ["M", "F", "M", "F"], "RACE": ["WHITE", "ASIAN", "WHITE", "ASIAN"], "ARMCD": ["DRUG", "PLACEBO", "DRUG", "PLACEBO"], "ARM": ["Active Drug 10mg", "Placebo", "Active Drug 10mg", "Placebo"], }) validation = validate_sdtm(data=dm_data_with_issues, domain="DM").interrogate() validation ``` 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" /> 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" />
Pointblank Validation
SDTM DM Validation Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_not_null()
STUDYID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
yYW5zZm9ybT0idHJhbnNsYXRlKDM0Ljg5OTQ5NiwgMzIuMTUzMzAzKSByb3RhdGUoLTMyMC4wMDAwMDApIHRyYW5zbGF0ZSgtMzQuODk5NDk2LCAtMzIuMTUzMzAzKSAiIHBvaW50cz0iMzQuMzk5NDk2MiA4LjU0MTYwNDY5IDM1LjM5OTQ5NjIgOC41NDE2MDQ2OSAzNS4zOTk0OTYyIDU1Ljc2NTAwMTkgMzQuMzk5NDk2MiA1NS43NjUwMDE5Ij48L3BvbHlnb24+CiAgICAgICAgPC9nPgogICAgPC9nPgo8L3N2Zz4=" /> col_vals_not_null()
DOMAIN -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C66 3
col_vals_not_null()
USUBJID -- ✓ 4 3
0.75		 1
0.25		 -- -- -- CSV
#4CA64C 4
col_vals_not_null()
SUBJID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 5
col_vals_not_null()
SITEID -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 6
col_vals_not_null()
SEX -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 7
col_vals_not_null()
ARMCD -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 8
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" /> col_vals_not_null()
ARM -- ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C66 9
col_vals_in_set()
DOMAIN DM ✓ 4 3
0.75		 1
0.25		 -- -- -- CSV
#4CA64C 10
col_vals_expr()

STUDYID length <= 20

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 11
4yMzU1MTU3IDI4LjE5ODUxOTgsMTQuNjAyMTg2MyAyNS41MTMzMjcxLDE0LjYwMjE4NjMgTDIyLjE2ODI3MDEsMTQuNjAyMTg2MyBaIiBpZD0ibGFtYmRhIiBmaWxsPSIjMDAwMDAwIiBmaWxsLXJ1bGU9Im5vbnplcm8iIC8+CiAgICAgICAgPC9nPgogICAgPC9nPgo8L3N2Zz4=" /> col_vals_expr()

DOMAIN length <= 2

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 12
col_vals_expr()

USUBJID length <= 40

 -- COLUMN EXPR ✓ 4 3
0.75		 0
0.00		 -- -- -- --
#4CA64C 13
col_vals_expr()

SUBJID length <= 20

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 14
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" /> col_vals_expr()

RFSTDTC length <= 64

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 15
col_vals_expr()

SITEID length <= 20

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 16
yMTAzNzc0LDI2LjYyMzQ4NSBMMjAuNDk1NzQxNSw1MS4zOTc4MTM3IEwyNC4xNTQzOTc3LDUxLjM5NzgxMzcgTDMyLjkzNTE3MjQsMzEuMDEzODcyNCBMMzkuNDY4NDg2OSw0Ny4yNjg3NTg5IEM0MC40NjgwODM3LDQ5Ljc2NDQ4NTkgNDIuODk4NDc2Nyw1MS4zOTc4MTM3IDQ1LjU4MzY2OTMsNTEuMzk3ODEzNyBMNDguOTI4NzI2NCw1MS4zOTc4MTM3IEw0OC45Mjg3MjY0LDQ4LjA1Mjc1NjcgTDQ1LjU4MzY2OTMsNDguMDUyNzU2NyBDNDQuMjUwODczMiw0OC4wNTI3NTY3IDQzLjA0ODc0MzMsNDcuMjQ5MTU4NiA0Mi41NTIyMTE0LDQ2LjAxNDM2MjUgTDMxLjYyODUwOTUsMTguNzMxMjQxMSBDMzAuNjI4OTEyOCwxNi4yMzU1MTU3IDI4LjE5ODUxOTgsMTQuNjAyMTg2MyAyNS41MTMzMjcxLDE0LjYwMjE4NjMgTDIyLjE2ODI3MDEsMTQuNjAyMTg2MyBaIiBpZD0ibGFtYmRhIiBmaWxsPSIjMDAwMDAwIiBmaWxsLXJ1bGU9Im5vbnplcm8iIC8+CiAgICAgICAgPC9nPgogICAgPC9nPgo8L3N2Zz4=" /> col_vals_expr()

AGEU length <= 10

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 17
col_vals_expr()

SEX length <= 2

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 18
col_vals_expr()

RACE length <= 60

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 19
col_vals_expr()

ARMCD length <= 20

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C 20
col_vals_expr()

ARM length <= 200

 -- COLUMN EXPR ✓ 4 4
1.00		 0
0.00		 -- -- -- --
#4CA64C66 21
xIEwzMS4xNjYyOTc5LDUuMDkxMjA5MzQgQzMxLjQ0ODEzNjIsNC45MjgxNjE5OSAzMS44MDkxNjkxLDUuMDIzNjYxNDMgMzEuOTc0NTQ1NSw1LjMwNTQ5OTc1IEwzMy4xNjcxMTgyLDcuMzMxOTQxMzcgQzMzLjI0ODY0MTMsNy40NjkzNjY4OSAzMy4yNzE5MzQxLDcuNjMyNDE0MjMgMzMuMjMwMDA3NCw3Ljc4NjE0NDAxIEMzMy4xOTA0MTA0LDcuOTM5ODc0MzkgMzMuMDkwMjUyOCw4LjA3MjY0MTEyIDMyLjk1MjgyNzgsOC4xNTE4MzUxMSBMMjYuMzMwNzg4MiwxMS45ODgwOTk0IEwzMi45NTI4Mjc4LDE1LjgyNDM2MzggQzMzLjA4NzkyMzcsMTUuOTA1ODg3NCAzMy4xODgwODEzLDE2LjAzNjMyNTEgMzMuMjMwMDA3NCwxNi4xOTAwNTQ5IEMzMy4yNjk2MDUsMTYuMzQzNzg1MiAzMy4yNDg2NDEzLDE2LjUwNjgzMiAzMy4xNjcxMTgyLDE2LjY0NDI1NzUgTDMxLjk3NDU0NTUsMTguNjcwNjk5MSBDMzEuODA5MTY5MSwxOC45NTI1MzggMzEuNDQ4MTM2MiwxOS4wNTAzNjYgMzEuMTY2Mjk3OSwxOC44ODQ5ODk1IEwyNC41NDQyNTgyLDE1LjEwMjI5NzkgTDI0LjU0NDI1ODIsMjIuNzIxMjUzOCBDMjQuNTQ0MjU4MiwyMy4wNTIwMDY3IDI0LjI3ODcyNDcsMjMuMzE3NTQwMiAyMy45NDc5NzE4LDIzLjMxNzU0MDIgWiIgaWQ9ImFzdGVyaXNrIiAvPgogICAgICAgICAgICA8L2c+CiAgICAgICAgPC9nPgogICAgPC9nPgo8L3N2Zz4=" /> col_vals_regex()
RFSTDTC ^(\d{4})(-\d{2}(-\d{2}(T\d{2}(:\d{2}(:\d{2})?)?)?)?)?$ ✓ 4 3
0.75		 1
0.25		 -- -- -- CSV
Three steps now report a failing unit: the [col_vals_not_null()](../../reference/Validate.col_vals_not_null.md#pointblank.Validate.col_vals_not_null) check on `USUBJID`, the [col_vals_in_set()](../../reference/Validate.col_vals_in_set.md#pointblank.Validate.col_vals_in_set) check on `DOMAIN`, and the ISO 8601 [col_vals_regex()](../../reference/Validate.col_vals_regex.md#pointblank.Validate.col_vals_regex) check on `RFSTDTC`. Each failing step links to the specific rows so you can trace a conformance issue back to the offending records. ## Converting SDTM Templates to MetadataImport If you prefer to work with the standard [MetadataImport](../../reference/MetadataImport.md#pointblank.MetadataImport) interface (for example, to use [to_schema()](../../reference/MetadataImport.md#pointblank.MetadataImport.to_schema) or combine SDTM metadata with other sources), you can convert a domain template: ``` python from pointblank.metadata import sdtm_to_metadata # Convert the DM template to a MetadataImport dm_meta = sdtm_to_metadata(domain="DM", study_id="STUDY01") print(f"Format: {dm_meta.source_format}") print(f"Dataset: {dm_meta.dataset_name}") print(f"Variables: {len(dm_meta.variables)}") # Generate a schema from it schema = dm_meta.to_schema() print(f"Schema columns: {len(schema.columns)}") ``` Format: cdisc_sdtm Dataset: DM Variables: 26 Schema columns: 26 # ADaM Dataset Templates The Analysis Data Model builds on top of SDTM by adding derived variables, population flags, and analysis-specific structures. ADaM datasets are the basis for statistical analyses in clinical trials, and their structure is tightly specified to ensure reproducibility and traceability back to the source data. Pointblank includes templates for four ADaM dataset structures: **ADSL** (subject-level analysis), **BDS** (Basic Data Structure for repeated measures), **ADAE** (adverse events analysis), and **ADTTE** (time-to-event analysis). ## Available ADaM Datasets ``` python from pointblank.metadata import list_adam_datasets, get_adam_dataset # List all available ADaM dataset templates datasets = list_adam_datasets() for d in datasets: template = get_adam_dataset(d) req_count = sum(1 for v in template.variables if v.required) flag_count = sum(1 for v in template.variables if v.is_population_flag) print(f" {d}: {template.label}") print(f" {req_count} required vars, {flag_count} population flags") ``` ADAE: Adverse Event Analysis Dataset 5 required vars, 0 population flags ADSL: Subject Level Analysis Dataset 5 required vars, 7 population flags ADTTE: Time-to-Event Analysis Dataset 8 required vars, 0 population flags BDS: Basic Data Structure 5 required vars, 0 population flags ## ADSL: Subject-Level Analysis ADSL is the foundational ADaM dataset. It contains one row per subject with all the key demographic and treatment information needed for analysis. Every other ADaM dataset merges back to ADSL for population definitions. ``` python adsl_template = get_adam_dataset("ADSL") print(f"Dataset class: {adsl_template.dataset_class}") print(f"\nPopulation flags:") for var in adsl_template.variables: if var.is_population_flag: print(f" {var.name}: {var.label}") ``` Dataset class: ADSL Population flags: SAFFL: Safety Population Flag ITTFL: Intent-To-Treat Population Flag EFFFL: Efficacy Population Flag RANDFL: Randomized Population Flag ENRLFL: Enrolled Population Flag PPROTFL: Per-Protocol Population Flag COMPLFL: Completers Population Flag Population flags (SAFFL, ITTFL, EFFFL, etc.) define which subjects belong to each analysis population. They must contain only the values "Y" or "N", with no nulls. Pointblank's ADaM validation checks this automatically. ## Full ADaM Validation The [validate_adam()](../../reference/validate_adam.md#pointblank.validate_adam) function generates comprehensive checks tailored to each dataset type: ``` python import polars as pl from pointblank.metadata import validate_adam # Create a minimal ADSL dataset adsl_data = pl.DataFrame({ "STUDYID": ["STUDY01"] * 5, "USUBJID": [f"STUDY01-{i:03d}" for i in range(1, 6)], "SUBJID": [f"{i:03d}" for i in range(1, 6)], "SITEID": ["SITE01", "SITE01", "SITE02", "SITE02", "SITE01"], "TRT01P": ["Drug A", "Placebo", "Drug A", "Placebo", "Drug A"], "TRT01A": ["Drug A", "Placebo", "Drug A", "Placebo", "Drug A"], "AGE": [45, 62, 38, 55, 48], "AGEU": ["YEARS"] * 5, "SEX": ["M", "F", "M", "F", "M"], "RACE": ["WHITE", "BLACK OR AFRICAN AMERICAN", "ASIAN", "WHITE", "WHITE"], "SAFFL": ["Y", "Y", "Y", "Y", "N"], "ITTFL": ["Y", "Y", "Y", "Y", "Y"], "EFFFL": ["Y", "Y", "N", "Y", "N"], "TRTSDT": ["2024-01-15", "2024-01-20", "2024-02-01", "2024-02-10", "2024-02-15"], "TRTEDT": ["2024-06-15", "2024-06-20", "2024-07-01", "2024-07-10", "2024-07-15"], }) # Run ADaM ADSL validation validation = validate_adam(data=adsl_data, dataset="ADSL").interrogate() validation ```
Pointblank Validation
ADaM ADSL Validation Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_not_null()
STUDYID -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
col_vals_not_null()
USUBJID -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 3
col_vals_not_null()
SUBJID -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_vals_not_null()
SITEID -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 5
col_vals_not_null()
TRT01P -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 6
col_vals_in_set()
SAFFL Y, N ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 7
col_vals_in_set()
ITTFL Y, N ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 8
col_vals_in_set()
EFFFL Y, N ✓ 5 5
1.00		 0
0.00		 -- -- -- --
#4CA64C 9
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" /> col_vals_not_null()
TRT01P -- ✓ 5 5
1.00		 0
0.00		 -- -- -- --
## ADaM Validation Checks by Dataset Type The checks generated by [validate_adam()](../../reference/validate_adam.md#pointblank.validate_adam) vary depending on the dataset type. Each type has its own domain-specific rules in addition to the common required-variable and population-flag checks: | Dataset | Specific Checks | |-----------|-----------------------------------------------| | **ADSL** | TRT01P non-null, all population flags are Y/N | | **BDS** | PARAMCD length at most 8 characters | | **ADAE** | TRTEMFL is Y/N, AESEQ is positive | | **ADTTE** | CNSR is 0 or 1, AVAL (time) is non-negative | Here is an example validating a BDS (Basic Data Structure) dataset: ``` python # Create a minimal BDS dataset (e.g., ADLB - laboratory analysis) bds_data = pl.DataFrame({ "STUDYID": ["STUDY01"] * 6, "USUBJID": ["STUDY01-001"] * 3 + ["STUDY01-002"] * 3, "PARAMCD": ["ALT", "AST", "BILI"] * 2, "PARAM": [ "Alanine Aminotransferase (U/L)", "Aspartate Aminotransferase (U/L)", "Bilirubin (umol/L)", ] * 2, "AVAL": [25.0, 30.0, 12.0, 45.0, 38.0, 15.0], "ABLFL": ["Y", "Y", "Y", "N", "N", "N"], "ANL01FL": ["Y"] * 6, "TRTA": ["Drug A"] * 3 + ["Placebo"] * 3, }) validation = validate_adam(data=bds_data, dataset="BDS").interrogate() validation ``` nJvdyIgZmlsbD0iIzAwMDAwMCIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoMTUuOTI5MjMwLCAxMS44OTQ3MzcpIHJvdGF0ZSgtMTgwLjAwMDAwMCkgdHJhbnNsYXRlKC0xNS45MjkyMzAsIC0xMS44OTQ3MzcpICIgLz4KICAgICAgICA8L2c+CiAgICA8L2c+Cjwvc3ZnPg==" /> 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" />
Pointblank Validation
ADaM BDS Validation Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
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## Structural Validation Like SDTM, ADaM provides a quick structural check via [validate_adam_structure()](../../reference/validate_adam_structure.md#pointblank.validate_adam_structure): ``` python from pointblank.metadata import validate_adam_structure result = validate_adam_structure(adsl_data, dataset="ADSL") print(f"Valid: {result['valid']}") print(f"Missing required: {result['missing_required']}") print(f"Population flags present: {result.get('population_flags_present', [])}") ``` Valid: True Missing required: [] Population flags present: [] ## Converting ADaM Templates to MetadataImport The [adam_to_metadata()](../../reference/adam_to_metadata.md#pointblank.adam_to_metadata) function converts an ADaM template into the standard [MetadataImport](../../reference/MetadataImport.md#pointblank.MetadataImport) format, giving you access to [to_schema()](../../reference/MetadataImport.md#pointblank.MetadataImport.to_schema) and [to_validate()](../../reference/Contract.md#pointblank.Contract.to_validate): ``` python from pointblank.metadata import adam_to_metadata # Convert ADSL template to MetadataImport adsl_meta = adam_to_metadata(dataset="ADSL", study_id="STUDY01") print(f"Format: {adsl_meta.source_format}") print(f"Version: {adsl_meta.source_version}") print(f"Variables: {len(adsl_meta.variables)}") # You can also use it through the import_metadata dispatcher meta = pb.import_metadata("ADSL", format="cdisc_adam", dataset="ADSL") print(f"Same result: {meta.dataset_name}") ``` Format: cdisc_adam Version: IG 1.1 Variables: 30 Same result: ADSL # Frictionless Data Packages While not a clinical standard, Frictionless Data Packages are widely used in open data and research contexts. They describe tabular data with JSON schemas that specify column types, constraints (minimum, maximum, enum, pattern), and primary keys. Pointblank imports these seamlessly. ## Importing a Frictionless Schema The bundled `datapackage.json` example describes a transactions table. Importing it yields a [MetadataImport](../../reference/MetadataImport.md#pointblank.MetadataImport) with one [VariableMetadata](../../reference/VariableMetadata.md#pointblank.VariableMetadata) per column: ``` python # Import the bundled Frictionless Data Package example datapackage_path = pb.load_metadata_example("datapackage.json") meta = pb.import_metadata(datapackage_path, format="frictionless") print(f"Dataset: {meta.dataset_name}") for v in meta.variables: print(f" {v.name:16s} {v.dtype}") ``` Dataset: transactions transaction_id String customer_id String amount Float64 quantity Int64 category String sale_date Date discount_pct Float64 email String Frictionless constraints map directly onto Pointblank validation steps: | Frictionless constraint | Pointblank step | |----|----| | `"required": true` | [col_vals_not_null()](../../reference/Validate.col_vals_not_null.md#pointblank.Validate.col_vals_not_null) | | `"unique": true` | [rows_distinct()](../../reference/Validate.rows_distinct.md#pointblank.Validate.rows_distinct) | | `"minimum": 0` | `col_vals_ge(value=0)` | | `"maximum": 100` | `col_vals_le(value=100)` | | `"pattern": "..."` | `col_vals_regex(pattern="...")` | | `"enum": [...]` | `col_vals_in_set(set=[...])` | The constraint mapping is direct and complete. Every constraint expressible in a Frictionless Table Schema has a corresponding Pointblank validation step, making the translation lossless. A standalone Table Schema (without the surrounding data package wrapper) imports the same way with `format="table_schema"`. ## CSVW (CSV on the Web) The W3C's CSVW standard provides similar capabilities to Frictionless but uses JSON-LD and aligns with linked data principles. Pointblank imports CSVW metadata with the same interface: ``` python # Import the bundled CSVW example csvw_path = pb.load_metadata_example("weather_csvw.json") meta = pb.import_metadata(csvw_path, format="csvw") # CSVW column descriptors become VariableMetadata print(f"Dataset: {meta.dataset_name} ({len(meta.variables)} variables)") print("Columns:", ", ".join(v.name for v in meta.variables)) ``` Dataset: weather_observations (7 variables) Columns: station_id, timestamp, temperature_c, humidity_pct, wind_speed_kmh, precipitation_mm, condition # Exporting Metadata Pointblank can also export validation metadata in Frictionless format. This is useful when you want to share data quality expectations with tools that understand the Frictionless ecosystem: ``` python import json import tempfile from pathlib import Path # Read metadata from the bundled SAS Transport (XPT) example xpt_path = pb.load_metadata_example("dm.xpt") meta = pb.import_metadata(xpt_path, format="xpt") # Export it as a Frictionless Table Schema out_path = Path(tempfile.gettempdir()) / "dm_table_schema.json" pb.export_metadata(meta, out_path, format="frictionless") # Inspect the exported schema: how many fields, and the first two definitions schema = json.loads(out_path.read_text()) print(f"Exported {len(schema['fields'])} field definitions. First two:") print(json.dumps(schema["fields"][:2], indent=2)) ``` Exported 12 field definitions. First two: [ { "name": "STUDYID", "type": "string", "title": "Study Identifier", "constraints": { "maxLength": 6 } }, { "name": "DOMAIN", "type": "string", "title": "Domain Abbreviation", "constraints": { "maxLength": 2 } } ] The exported document contains the column definitions and constraints from the original metadata, formatted as a valid Frictionless Table Schema that other tools can consume. # Combining Multiple Metadata Sources In practice, clinical data validation often combines metadata from multiple sources. The Define-XML provides the authoritative variable definitions, but you might also want to check against SDTM domain rules and controlled terminology packages. Pointblank supports this by letting you compose validation workflows from different metadata sources: ``` python from pointblank.metadata import validate_sdtm # Load the Define-XML for variable-level constraints package = pb.import_metadata(pb.load_metadata_example("define.xml"), format="cdisc_define") dm_meta = package["DM"] # Generate validation from Define-XML metadata validation = dm_meta.to_validate(data=dm_data) # The SDTM template adds domain-specific rules not in the Define-XML # (ISO 8601 checks, sequence number rules, etc.) sdtm_validation = validate_sdtm(data=dm_data, domain="DM") # Run both and compare how many checks each contributes define_results = validation.interrogate() sdtm_results = sdtm_validation.interrogate() print(f"Define-XML checks: {len(define_results.validation_info)}") print(f"SDTM template checks: {len(sdtm_results.validation_info)}") ``` Define-XML checks: 7 SDTM template checks: 25 This layered approach gives you the flexibility to apply different levels of validation depending on your needs. The Define-XML checks enforce what was specifically documented for your study, while the SDTM template checks enforce the broader standard requirements that apply universally. # Null Flavors and Structured Missingness Clinical data uses standardized HL7/CDISC *null flavors* to record *why* a value is absent (e.g., `"NASK"` = not asked, `"UNK"` = unknown, `"NA"` = not applicable). Pointblank ships a pre-built MissingSpec for these codes via [MissingSpec.from_cdisc_null_flavors()](../../reference/MissingSpec.md#pointblank.MissingSpec.from_cdisc_null_flavors): ``` python cdisc = pb.MissingSpec.from_cdisc_null_flavors() print("NASK ->", cdisc.reason_for("NASK")) # not_asked print("UNK ->", cdisc.reason_for("UNK")) # unknown print("boundary codes:", cdisc.values_for_category("boundary")) ``` NASK -> not_asked UNK -> unknown boundary codes: ['PINF', 'NINF'] This spec can be passed to [missing_vals_tbl()](../../reference/missing_vals_tbl.md#pointblank.missing_vals_tbl) for a reason-by-reason breakdown, or to the `col_vals_*()` and dedicated missingness validation methods ([col_pct_missing()](../../reference/Validate.col_pct_missing.md#pointblank.Validate.col_pct_missing), [col_missing_coded()](../../reference/Validate.col_missing_coded.md#pointblank.Validate.col_missing_coded), [col_missing_only_coded()](../../reference/Validate.col_missing_only_coded.md#pointblank.Validate.col_missing_only_coded), [col_missing_consistent()](../../reference/Validate.col_missing_consistent.md#pointblank.Validate.col_missing_consistent)) to validate data while accounting for the null flavor codes. See the *Missing Values Reporting* and *Validation Methods* articles for the full set of capabilities. # Conclusion CDISC data validation with Pointblank covers the full spectrum of clinical trial data management: from parsing Define-XML documents and controlled terminology packages to validating individual datasets against SDTM and ADaM structural rules. The built-in domain templates encode years of regulatory guidance into ready-to-use validation workflows, letting you check data compliance with a single function call. For teams preparing regulatory submissions, this means catching structural issues, date format errors, and terminology violations early in the data pipeline, well before the formal submission review process begins.