ConformanceReport

The results of a CDISC conformance check.

Usage

Source

ConformanceReport(
    validations=dict(),
    package=None,
    agency=None,
    core=None,
    native_result=None,
)

A ConformanceReport holds the results of checking a set of CDISC datasets for conformance to their standard. It is returned by validate_sdtmig(), validate_cdisc_submission() and the SubmissionPackage method validate_conformance(), and is rarely made directly. The report has one of three forms, depending on the engine that did the checks:

• Built-in rules (is_rules is True): the results of Pointblank’s bundled catalog of SDTMIG rules. Each rule has a status of "pass", "fail", "error" (the rule couldn’t be evaluated), "not_applicable" (an input that it needs, such as a Define-XML, wasn’t given) or "not_supported". The rules that check values give the failing records, which are in findings_df() and get_findings_table().
• CDISC CORE (is_core is True): the results of the CDISC CORE engine, with CORE’s own rule statuses (such as "SUCCESS", "ISSUE REPORTED" and "SKIPPED"). A report of this form can also be made from a saved CORE report with from_core_report().
• Validate-based: a Validate validation for each dataset, from SubmissionPackage.validate_conformance(engine="validate"). Each validation can be had with get_validation().

The methods summary(), issues() and all_passed() work for all three forms. In a notebook, a report of the built-in rules is shown as the table of get_tabular_report(). A CORE report is shown as a short summary with a table of its issues, and a Validate-based report as the validation report of each dataset. Printing a report of any form gives a text summary.

Parameters

validations: dict[str, Validate] = dict()

The validation for each dataset, keyed by dataset name, for a Validate-based report. The default is an empty dict.

package: SubmissionPackage | None = None

The SubmissionPackage that was checked. The default is None.

agency: str | None = None

The regulatory agency, such as "FDA", which is only used as a label in the report. The default is None.

core: ParsedCoreReport | None = None

CORE’s results, for a CDISC CORE report. The default is None.

native_result: NativeConformanceResult | None = None

The results of the rules, for a report of the built-in rules. The default is None.

Examples

Let’s check a small DM dataset with the built-in rules. It has a date that isn’t in the ISO 8601 format, a negative age and a SEX value that isn’t in its codelist:

import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "RFSTDTC": ["2024-01-15", "15JAN2024", "2024-01-17"],
        "AGE": [34, 51, -38],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

print(report)

ConformanceReport (Built-in Rules)
  sdtmig 3-4
  362 rules (fail=10, not_applicable=12, pass=340)
  10 issues — FAIL

Ten of the 362 rules fail. Most of them are about what the dataset lacks, such as the DM variables SUBJID and RACE and the TS and TA datasets. The failing records are found by three of the rules:

report.findings_df().select("rule_id", "row_index", "checked_column", "checked_value")

shape: (3, 4)

rule_id	row_index	checked_column	checked_value
str	i64	str	str
"SDTM-007"	2	"SEX"	"FEMALE"
"SDTM-011"	1	"RFSTDTC"	"15JAN2024"
"SDTM-195"	2	"AGE"	"-38"

The row_index of a finding is the 0-based position of the record in its dataset. To see the results of all the rules as a table, use get_tabular_report().

See Also

validate_sdtmig()

Check SDTM datasets with the built-in rules.

validate_cdisc_submission()

Check a folder of datasets with CDISC CORE.

SubmissionPackage

A set of datasets to check together.

Attributes

Name	Description
n_datasets	The number of datasets that were checked.
is_core	Whether the report holds the results of CDISC CORE.
is_rules	Whether the report holds the results of Pointblank’s built-in rules.

n_datasets

The number of datasets that were checked.

n_datasets: int

For a report of the built-in rules, this is the number of datasets that were given, and for a Validate-based report, the number of validations. For a CDISC CORE report, it is the number of datasets in CORE’s Dataset_Details. Datasets that a rule requires but that are missing (such as TS) aren’t counted. This is a property, so it’s used without parentheses.

Examples

Let’s check a package of two datasets with the built-in rules:

import pointblank as pb
import polars as pl

dm = pl.DataFrame({"STUDYID": ["STUDY01"], "USUBJID": ["STUDY01-001"]})
ae = pl.DataFrame({"STUDYID": ["STUDY01"], "USUBJID": ["STUDY01-001"], "AESEQ": [1]})

report = pb.validate_sdtmig({"DM": dm, "AE": ae})

report.n_datasets

2

The two datasets were checked, though some rules are about the TS and TA datasets, which the package doesn’t have.

is_core

Whether the report holds the results of CDISC CORE.

is_core: bool

This is True for the report of validate_cdisc_submission(), of SubmissionPackage.validate_conformance(engine="core") and of from_core_report(). Some methods only work for one form of report, so this tells code which form it has. This is a property, so it’s used without parentheses.

Examples

Let’s check a DM dataset with the built-in rules and look at the form of the report:

import pointblank as pb
import polars as pl

dm = pl.DataFrame({"STUDYID": ["STUDY01"], "USUBJID": ["STUDY01-001"], "SEX": ["F"]})

report = pb.validate_sdtmig({"DM": dm})

print(report.is_core, report.is_rules)

False True

The report is of the built-in rules, so is_core is False and is_rules is True.

is_rules

Whether the report holds the results of Pointblank’s built-in rules.

is_rules: bool

This is True for the report of validate_sdtmig() and of SubmissionPackage.validate_conformance() with its default engine (when the package’s standard has a bundled rules catalog). Only these reports have findings_df(), get_findings_table() and get_tabular_report(). This is a property, so it’s used without parentheses.

Examples

Let’s check a package with each of the two Pointblank engines:

import pointblank as pb
import polars as pl

dm = pl.DataFrame({"STUDYID": ["STUDY01"], "USUBJID": ["STUDY01-001"], "SEX": ["F"]})
study = pb.SubmissionPackage(datasets={"DM": dm})

print(study.validate_conformance().is_rules)
print(study.validate_conformance(engine="validate").is_rules)

True
False

The "validate" engine gives a report of Validate validations, so its is_rules is False, as is its is_core.

Methods

Name	Description
all_passed()	Check whether the datasets conform, with nothing failing.
summary()	Summarize the results of the conformance check as a dict.
get_tabular_report()	Make a table of the results of the built-in rules.
get_findings_table()	Make a table of the failing records found by the built-in rules.
rules()	Get the result of each rule.
issues()	List the issues that the conformance check found.
findings()	Get the failing records that the conformance check found.
findings_df()	Get the failing records found by the built-in rules, as a DataFrame.
get_validation()	Get the validation of one dataset from a Validate-based report.
to_json()	Save the report as a JSON file.
to_excel()	Save the report as an Excel workbook.
from_core_report()	Make a report from the JSON report of a CDISC CORE run.

all_passed()

Check whether the datasets conform, with nothing failing.

Usage

Source

all_passed()

This gives a single answer for a conformance check, as is needed in a script or a pipeline that should stop when the data don’t conform. What counts as passing depends on the form of the report:

• For the built-in rules, no rule has the status "fail". A failing rule of "Warning" sensitivity counts too. Rules that are not applicable or couldn’t be evaluated don’t.
• For CDISC CORE, no rule reported an issue or an execution error.
• For a Validate-based report, every step of every validation passed (with no failing test units).

Returns

bool

True if nothing failed, otherwise False.

Examples

Let’s check a DM dataset with the built-in rules:

import pointblank as pb
import polars as pl

dm = pl.DataFrame({"STUDYID": ["STUDY01"], "USUBJID": ["STUDY01-001"], "SEX": ["F"]})

report = pb.validate_sdtmig({"DM": dm})

report.all_passed()

False

The dataset lacks many of the variables that DM requires, and the TS and TA datasets are missing, so some rules fail. The rules that fail can be listed with rules(), as in report.rules(status="fail").

summary()

Summarize the results of the conformance check as a dict.

Usage

Source

summary()

The summary has the counts that are shown when a report is printed, in a form that code can use, such as for logging the results of a run or for comparing two runs. Its keys depend on the form of the report.

Returns

dict

For a report of the built-in rules, a dict with the keys standard, version, engine (which is "built-in"), ct_packages (the controlled terminology packages that were used), n_rules, status_counts (the number of rules with each status), n_issues (the total number of issues) and all_passed.

For a CDISC CORE report, a dict with the keys standard, version, engine_version (the version of CORE), engine (which is "core"), n_rules, status_counts, n_issues, n_datasets and all_passed.

For a Validate-based report, a dict with an entry for each dataset. Each entry is a dict with the keys n_steps, n_steps_failed, n_failed (the number of failing test units) and all_passed.

Examples

Let’s check a small DM dataset with the built-in rules and summarize the results:

import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "RFSTDTC": ["2024-01-15", "15JAN2024", "2024-01-17"],
        "AGE": [34, 51, -38],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

report.summary()

{'standard': 'sdtmig',
 'version': '3-4',
 'engine': 'built-in',
 'ct_packages': ['sdtm-ct-2024-09-27'],
 'n_rules': 362,
 'status_counts': {'pass': 340, 'fail': 10, 'not_applicable': 12},
 'n_issues': 10,
 'all_passed': False}

The version is given as "3-4", the form that the rules catalog uses. The issue count is the sum of the issues of all the rules: each failing record counts for a rule that checks values, and a failing rule counts once for a rule that checks the presence of variables or datasets.

get_tabular_report()

Make a table of the results of the built-in rules.

Usage

Source

get_tabular_report()

The table has a row for each rule in the catalog, so it shows at a glance which rules failed and how many issues each one found. It is how a report of the built-in rules is shown in a notebook. For the failing records themselves, use get_findings_table() or findings_df().

Each row has a colored bar for its status (green for a pass, red for a failure, amber for an error and gray for a rule that is not applicable or not supported), the rule’s identifier, its domain, its type (such as Record for a check of values or Variable for a check that variables are present), its number of issues and its description. The failing rules come first, then the rules with errors, the passing rules and the rules that are not applicable or not supported. The title has a PASS or FAIL badge, and the subtitle has the standard and its version, the agency (if one was given) and the number of rules with each status.

Returns

GT

The table, as a Great Tables GT object.

Raises

TypeError

If the report isn’t of the built-in rules.

Examples

Let’s check a small DM dataset with the built-in rules and make the table:

import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "RFSTDTC": ["2024-01-15", "15JAN2024", "2024-01-17"],
        "AGE": [34, 51, -38],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

report.get_tabular_report()

CDISC Conformance FAIL
SDTMIG 3-4 · 340 passed · 10 failed · 12 n/a
	Rule	Domain	Type	Issues	Description
#FF3300	SDTM-007	DM	Record	1	SEX in DM must use values from the CDISC controlled terminology codelist SEX.
#FF3300	SDTM-011	DM	Record	1	RFSTDTC in DM must be in ISO 8601 extended datetime format when present.
#FF3300	SDTM-031	DM	Variable	1	DM domain must contain all required Identifier and Topic variables.
#FF3300	SDTM-032	DM	Variable	1	DM domain must contain required demographic variables (SEX, RACE, COUNTRY).
#FF3300	SDTM-033	DM	Variable	1	DM domain must contain RFSTDTC and RFENDTC (reference start/end dates).
#FF3300	SDTM-034	DM	Variable	1	DM domain must contain SITEID, AGE, and AGEU.
#FF3300	SDTM-035	DM	Variable	1	DM domain must contain treatment arm variables (ARMCD, ARM, ACTARMCD, ACTARM).
#FF3300	SDTM-121	TS	Domain	1	TS domain must be present in every SDTM submission.
#FF3300	SDTM-122	TA	Domain	1	TA domain must be present in every SDTM submission.
#FF3300	SDTM-195	DM	Record	1	AGE must not be negative in the DM dataset.
#4CA64C	SDTM-001	DM	Domain	0	DM (Demographics) domain is required in every SDTM submission.
#4CA64C	SDTM-002	DM	Record	0	STUDYID must not be null in any domain.
#4CA64C	SDTM-003	DM	Record	0	DOMAIN must not be null in any SDTM dataset.
#4CA64C	SDTM-004	DM	Record	0	USUBJID must not be null in any SDTM dataset.
#4CA64C	SDTM-005	DM	Dataset	0	STUDYID must be consistent (same value) across all records in a dataset.
#4CA64C	SDTM-006	DM	Dataset	0	DOMAIN must be consistent (same value) across all records in a dataset.
#4CA64C	SDTM-008	DM	Record	0	RACE in DM must use values from the CDISC controlled terminology codelist RACE.
#4CA64C	SDTM-009	DM	Record	0	COUNTRY in DM must use ISO 3166 alpha-3 country codes (CDISC COUNTRY codelist).
#4CA64C	SDTM-010	DM	Record	0	DMDTC in DM must be in ISO 8601 extended datetime format.
#4CA64C	SDTM-012	DM	Record	0	RFENDTC in DM must be in ISO 8601 extended datetime format when present.
#4CA64C	SDTM-013	DM	Record	0	DTHDTC in DM must be in ISO 8601 extended datetime format when present.
#4CA64C	SDTM-014	DM	Record	0	DTHFL in DM must use values from the NY codelist (Y or null).
#4CA64C	SDTM-015	DM	Record	0	SUBJID must not be null in DM.
#4CA64C	SDTM-016	AE	Record	0	AETERM must not be null in AE.
#4CA64C	SDTM-017	AE	Record	0	AEDECOD must not be null in AE.
#4CA64C	SDTM-018	AE	Record	0	AESTDTC in AE must be in ISO 8601 extended datetime format when present.
#4CA64C	SDTM-019	AE	Record	0	AEENDTC in AE must be in ISO 8601 extended datetime format when present.
#4CA64C	SDTM-020	AE	Record	0	AESER in AE must use values from the NY codelist when present.
#4CA64C	SDTM-021	AE	Record	0	AEOUT in AE must use values from the OUT (Outcome of Event) codelist when present.
#4CA64C	SDTM-022	LB	Record	0	LBDTC in LB must be in ISO 8601 extended datetime format when present.
#4CA64C	SDTM-023	LB	Record	0	LBTEST must not be null in LB.
#4CA64C	SDTM-024	LB	Record	0	LBTESTCD must not be null in LB.
#4CA64C	SDTM-025	VS	Record	0	VSTEST must not be null in VS.
#4CA64C	SDTM-026	VS	Record	0	VSDTC in VS must be in ISO 8601 extended datetime format when present.
#4CA64C	SDTM-027	DM	Metadata	0	USUBJID must be present in every subject-level SDTM domain.
#4CA64C	SDTM-028	DM	Metadata	0	STUDYID must be present in every SDTM domain.
#4CA64C	SDTM-029	DM	Metadata	0	DOMAIN must be present in every SDTM domain.
#4CA64C	SDTM-030	DM	Record	0	ETHNIC in DM must use values from the ETHNIC codelist when present.
#4CA64C	SDTM-036	AE	Variable	0	AE domain must contain required Identifier and sequence variables.
#4CA64C	SDTM-037	AE	Variable	0	AE domain must contain the AE term and dictionary-derived variables.
#4CA64C	SDTM-038	AE	Variable	0	AE domain must contain the seriousness variable (AESER).
#4CA64C	SDTM-039	VS	Variable	0	VS domain must contain required Identifier and test variables.
#4CA64C	SDTM-040	VS	Variable	0	VS domain must contain result variables (VSORRES, VSORRESU, VSSTRESC, VSSTRESN, VSSTRESU).
#4CA64C	SDTM-041	LB	Variable	0	LB domain must contain required Identifier and test variables.
#4CA64C	SDTM-042	LB	Variable	0	LB domain must contain result variables (LBORRES, LBORRESU, LBSTRESC, LBSTRESN, LBSTRESU).
#4CA64C	SDTM-043	EG	Variable	0	EG domain must contain required Identifier and test variables.
#4CA64C	SDTM-044	DM	Variable	0	In any domain, STUDYID must appear before DOMAIN, which must appear before USUBJID.
#4CA64C	SDTM-045	VS	Variable	0	In findings domains (VS, LB, EG), the sequence variable (xSEQ) must appear after USUBJID.
#4CA64C	SDTM-046	DM	Variable	0	In DM, AGE must be a numeric variable.
#4CA64C	SDTM-047	VS	Variable	0	In VS, VSSTRESN must be a numeric variable.
#4CA64C	SDTM-048	LB	Variable	0	In LB, LBSTRESN must be a numeric variable.
#4CA64C	SDTM-061	DM	Record	0	AGEU in DM must use AGEU codelist when present.
#4CA64C	SDTM-062	DM	Record	0	BRTHDTC in DM must conform to ISO 8601 when present.
#4CA64C	SDTM-063	EX	Record	0	EXTRT must not be null in EX.
#4CA64C	SDTM-064	EX	Record	0	EXSTDTC in EX must conform to ISO 8601 when present.
#4CA64C	SDTM-065	EX	Record	0	EXENDTC in EX must conform to ISO 8601 when present.
#4CA64C	SDTM-066	EX	Record	0	EXDOSFRQ in EX must use FREQ codelist when present.
#4CA64C	SDTM-067	EX	Record	0	EXROUTE in EX must use ROUTE codelist when present.
#4CA64C	SDTM-068	EX	Record	0	EXDOSFRM in EX must use FRM codelist when present.
#4CA64C	SDTM-069	CM	Record	0	CMTRT must not be null in CM.
#4CA64C	SDTM-070	CM	Record	0	CMSTDTC in CM must conform to ISO 8601 when present.
#4CA64C	SDTM-071	CM	Record	0	CMENDTC in CM must conform to ISO 8601 when present.
#4CA64C	SDTM-072	CM	Record	0	CMROUTE in CM must use ROUTE codelist when present.
#4CA64C	SDTM-073	DS	Record	0	DSTERM must not be null in DS.
#4CA64C	SDTM-074	DS	Record	0	DSDECOD must not be null in DS.
#4CA64C	SDTM-075	DS	Record	0	DSSTDTC in DS must conform to ISO 8601 when present.
#4CA64C	SDTM-076	DS	Record	0	DSSEQ must not be null in DS.
#4CA64C	SDTM-077	MH	Record	0	MHTERM must not be null in MH.
#4CA64C	SDTM-078	MH	Record	0	MHSTDTC in MH must conform to ISO 8601 when present.
#4CA64C	SDTM-079	MH	Record	0	MHENDTC in MH must conform to ISO 8601 when present.
#4CA64C	SDTM-080	SV	Record	0	VISITNUM must not be null in SV.
#4CA64C	SDTM-081	SV	Record	0	SVSTDTC in SV must conform to ISO 8601 when present.
#4CA64C	SDTM-082	SV	Record	0	SVENDTC in SV must conform to ISO 8601 when present.
#4CA64C	SDTM-083	EG	Record	0	EGTEST must not be null in EG.
#4CA64C	SDTM-084	EG	Record	0	EGTESTCD must not be null in EG.
#4CA64C	SDTM-085	EG	Record	0	EGDTC in EG must conform to ISO 8601 when present.
#4CA64C	SDTM-086	AE	Record	0	AESEV in AE must use AESEV codelist when present.
#4CA64C	SDTM-089	AE	Record	0	AEACN in AE must use the ACN (Action Taken with Study Treatment) codelist when present.
#4CA64C	SDTM-090	AE	Record	0	AESEQ must not be null in AE.
#4CA64C	SDTM-091	VS	Record	0	VSTESTCD must not be null in VS.
#4CA64C	SDTM-092	VS	Record	0	VSSTRESU in VS must use VSRESU codelist when present.
#4CA64C	SDTM-093	VS	Record	0	VSBLFL in VS must use NY codelist when present.
#4CA64C	SDTM-094	LB	Record	0	LBSTRESU in LB must use the UNIT codelist when present.
#4CA64C	SDTM-095	LB	Record	0	LBBLFL in LB must use NY codelist when present.
#4CA64C	SDTM-096	LB	Record	0	LBNRIND in LB must use NRIND codelist when present.
#4CA64C	SDTM-097	PE	Record	0	PETEST must not be null in PE.
#4CA64C	SDTM-098	PE	Record	0	PEDTC in PE must conform to ISO 8601 when present.
#4CA64C	SDTM-099	IE	Record	0	IETEST must not be null in IE.
#4CA64C	SDTM-100	IE	Record	0	IECAT in IE must use IECAT codelist when present.
#4CA64C	SDTM-101	SUPP--	Record	0	RDOMAIN must not be null in SUPP-- datasets.
#4CA64C	SDTM-102	SUPP--	Record	0	QNAM must not be null in SUPP-- datasets.
#4CA64C	SDTM-103	SUPP--	Record	0	QLABEL must not be null in SUPP-- datasets.
#4CA64C	SDTM-104	TS	Record	0	TSPARMCD must not be null in TS.
#4CA64C	SDTM-105	TS	Record	0	TSPARM must not be null in TS.
#4CA64C	SDTM-106	DM	Dataset	0	STUDYID must be consistent (same value) across all records within each domain.
#4CA64C	SDTM-107	DM	Dataset	0	DOMAIN must be consistent (same value) across all records within each dataset.
#4CA64C	SDTM-108	EX	Variable	0	EX domain must contain required identifier and treatment variables.
#4CA64C	SDTM-109	EX	Variable	0	EX domain must contain timing variables EXSTDTC and EXENDTC.
#4CA64C	SDTM-110	CM	Variable	0	CM domain must contain required identifier and treatment variables.
#4CA64C	SDTM-112	DS	Variable	0	DS domain must contain required identifier and disposition variables.
#4CA64C	SDTM-113	MH	Variable	0	MH domain must contain required identifier and history variables.
#4CA64C	SDTM-114	SV	Variable	0	SV domain must contain required identifier and visit variables.
#4CA64C	SDTM-115	TS	Variable	0	TS domain must contain required parameter variables.
#4CA64C	SDTM-116	IE	Variable	0	IE domain must contain required inclusion/exclusion criteria variables.
#4CA64C	SDTM-117	PE	Variable	0	PE domain must contain required physical examination variables.
#4CA64C	SDTM-118	EX	Variable	0	EXDOSE must be a numeric variable in EX.
#4CA64C	SDTM-119	SV	Variable	0	VISITNUM must be a numeric variable in SV.
#4CA64C	SDTM-120	EG	Variable	0	EGSTRESN must be a numeric variable in EG.
#4CA64C	SDTM-123	TA	Record	0	ARMCD must not be null in TA.
#4CA64C	SDTM-124	TA	Record	0	ARM must not be null in TA.
#4CA64C	SDTM-125	TA	Record	0	TAETORD must not be null in TA.
#4CA64C	SDTM-126	TA	Record	0	EPOCH must not be null in TA.
#4CA64C	SDTM-127	TE	Record	0	ETCD must not be null in TE.
#4CA64C	SDTM-128	TE	Record	0	ELEMENT must not be null in TE.
#4CA64C	SDTM-129	TV	Record	0	VISITNUM must not be null in TV.
#4CA64C	SDTM-131	TI	Record	0	IETESTCD must not be null in TI.
#4CA64C	SDTM-132	TI	Record	0	IETEST must not be null in TI.
#4CA64C	SDTM-133	TI	Record	0	IECAT in TI must use IECAT controlled terminology when present.
#4CA64C	SDTM-136	AE	Record	0	AESDTH in AE must use NY controlled terminology when present.
#4CA64C	SDTM-137	AE	Record	0	AESHOSP in AE must use NY controlled terminology when present.
#4CA64C	SDTM-138	AE	Record	0	AESLIFE in AE must use NY controlled terminology when present.
#4CA64C	SDTM-139	EX	Record	0	EXSEQ must not be null in EX.
#4CA64C	SDTM-140	CM	Record	0	CMSEQ must not be null in CM.
#4CA64C	SDTM-141	MH	Record	0	MHSEQ must not be null in MH.
#4CA64C	SDTM-142	EG	Record	0	EGSEQ must not be null in EG.
#4CA64C	SDTM-143	VS	Record	0	VSSEQ must not be null in VS.
#4CA64C	SDTM-144	LB	Record	0	LBSEQ must not be null in LB.
#4CA64C	SDTM-145	PE	Record	0	PESEQ must not be null in PE.
#4CA64C	SDTM-146	IE	Record	0	IESEQ must not be null in IE.
#4CA64C	SDTM-147	AE	Dataset	0	AESEQ must be unique within USUBJID in AE.
#4CA64C	SDTM-148	EX	Dataset	0	EXSEQ must be unique within USUBJID in EX.
#4CA64C	SDTM-149	CM	Dataset	0	CMSEQ must be unique within USUBJID in CM.
#4CA64C	SDTM-150	LB	Dataset	0	LBSEQ must be unique within USUBJID in LB.
#4CA64C	SDTM-151	VS	Dataset	0	VSSEQ must be unique within USUBJID in VS.
#4CA64C	SDTM-152	AE	Variable	0	AESTDY must be numeric type in AE.
#4CA64C	SDTM-153	AE	Variable	0	AEENDY must be numeric type in AE.
#4CA64C	SDTM-154	LB	Variable	0	LBDY must be numeric type in LB.
#4CA64C	SDTM-155	VS	Variable	0	VSDY must be numeric type in VS.
#4CA64C	SDTM-156	EX	Variable	0	EXSTDY must be numeric type in EX.
#4CA64C	SDTM-157	CM	Variable	0	CMSTDY must be numeric type in CM.
#4CA64C	SDTM-158	TA	Variable	0	TA must contain required variables: STUDYID, DOMAIN, ARMCD, ARM, TAETORD, EPOCH.
#4CA64C	SDTM-159	TE	Variable	0	TE must contain required variables: STUDYID, DOMAIN, ETCD, ELEMENT.
#4CA64C	SDTM-160	TV	Variable	0	TV must contain required variables: STUDYID, DOMAIN, VISITNUM.
#4CA64C	SDTM-161	TI	Variable	0	TI must contain required variables: STUDYID, DOMAIN, IETESTCD, IETEST, IECAT.
#4CA64C	SDTM-163	VS	Variable	0	VISITNUM must be present in VS.
#4CA64C	SDTM-164	LB	Variable	0	VISITNUM must be present in LB.
#4CA64C	SDTM-165	EG	Variable	0	VISITNUM must be present in EG.
#4CA64C	SDTM-166	SUPP--	Record	0	QVAL must not be null in SUPP-- supplemental qualifier datasets.
#4CA64C	SDTM-167	TS	Record	0	TSSEQ must not be null in TS.
#4CA64C	SDTM-168	LB	Variable	0	LBSTNRLO must be numeric type in LB.
#4CA64C	SDTM-169	LB	Variable	0	LBSTNRHI must be numeric type in LB.
#4CA64C	SDTM-172	AE	Variable	0	AESEQ must be numeric type in AE.
#4CA64C	SDTM-173	EX	Variable	0	EXSEQ must be numeric type in EX.
#4CA64C	SDTM-174	CM	Variable	0	CMSEQ must be numeric type in CM.
#4CA64C	SDTM-175	MH	Variable	0	MHSEQ must be numeric type in MH.
#4CA64C	SDTM-176	VS	Variable	0	VSSEQ must be numeric type in VS.
#4CA64C	SDTM-177	LB	Variable	0	LBSEQ must be numeric type in LB.
#4CA64C	SDTM-178	EG	Variable	0	EGSEQ must be numeric type in EG.
#4CA64C	SDTM-179	PE	Variable	0	PESEQ must be numeric type in PE.
#4CA64C	SDTM-180	DS	Variable	0	DSSEQ must be numeric type in DS.
#4CA64C	SDTM-181	DM	Record	0	DOMAIN column value must equal 'DM' in the DM dataset.
#4CA64C	SDTM-182	AE	Record	0	DOMAIN column value must equal 'AE' in the AE dataset.
#4CA64C	SDTM-183	LB	Record	0	DOMAIN column value must equal 'LB' in the LB dataset.
#4CA64C	SDTM-184	VS	Record	0	DOMAIN column value must equal 'VS' in the VS dataset.
#4CA64C	SDTM-185	EX	Record	0	DOMAIN column value must equal 'EX' in the EX dataset.
#4CA64C	SDTM-186	CM	Record	0	DOMAIN column value must equal 'CM' in the CM dataset.
#4CA64C	SDTM-187	DS	Record	0	DOMAIN column value must equal 'DS' in the DS dataset.
#4CA64C	SDTM-188	MH	Record	0	DOMAIN column value must equal 'MH' in the MH dataset.
#4CA64C	SDTM-189	EG	Record	0	DOMAIN column value must equal 'EG' in the EG dataset.
#4CA64C	SDTM-190	TA	Record	0	DOMAIN column value must equal 'TA' in the TA dataset.
#4CA64C	SDTM-191	DM	Record	0	RFXSTDTC in DM must conform to ISO 8601 format when present (date of first exposure to trial treatment).
#4CA64C	SDTM-192	DM	Record	0	RFXENDTC in DM must conform to ISO 8601 format when present (date of last exposure to trial treatment).
#4CA64C	SDTM-193	DM	Record	0	RFICDTC in DM must conform to ISO 8601 format when present (date of informed consent).
#4CA64C	SDTM-194	DM	Record	0	RFPENDTC in DM must conform to ISO 8601 format when present (date of end of participation).
#4CA64C	SDTM-197	AE	Record	0	AESCONG (congenital anomaly flag) must use the NY codelist when present in AE.
#4CA64C	SDTM-198	AE	Record	0	AESDISAB (disability flag) must use the NY codelist when present in AE.
#4CA64C	SDTM-199	AE	Record	0	AESMIE (important medical event flag) must use the NY codelist when present in AE.
#4CA64C	SDTM-200	AE	Record	0	AECONTRT (concomitant treatment given flag) must use the NY codelist when present in AE.
#4CA64C	SDTM-202	MH	Record	0	MHPRESP (pre-specified) must use the NY codelist when present in MH.
#4CA64C	SDTM-203	MH	Record	0	MHOCCUR (occurrence) must use the NY codelist when present in MH.
#4CA64C	SDTM-204	EX	Record	0	EXOCCUR (occurrence) must use the NY codelist when present in EX.
#4CA64C	SDTM-205	EX	Variable	0	EXDOSTOT (total cumulative dose) must be a numeric variable in EX.
#4CA64C	SDTM-206	EX	Variable	0	EXDOSNO (dose number in a sequence) must be a numeric variable in EX.
#4CA64C	SDTM-207	CM	Record	0	CMOCCUR (occurrence) must use the NY codelist when present in CM.
#4CA64C	SDTM-208	CM	Variable	0	CMDOSE (dose per administration) must be a numeric variable in CM.
#4CA64C	SDTM-210	DS	Variable	0	DSSTDY (study day of disposition event) must be a numeric variable in DS.
#4CA64C	SDTM-211	PE	Record	0	PETESTCD (test short name) must not be null in the PE dataset.
#4CA64C	SDTM-212	PE	Variable	0	PESTRESN (numeric result in standard units) must be a numeric variable in PE.
#4CA64C	SDTM-213	IE	Record	0	IEORRES (original result) must not be null in the IE dataset.
#4CA64C	SDTM-214	IE	Record	0	IEDTC (date/time of IE assessment) must conform to ISO 8601 format when present in IE.
#4CA64C	SDTM-215	SV	Variable	0	SVSTDY (study day of visit start) must be a numeric variable in SV.
#4CA64C	SDTM-216	SV	Variable	0	SVENDY (study day of visit end) must be a numeric variable in SV.
#4CA64C	SDTM-218	TS	Variable	0	TSSEQ (sequence number) must be a numeric variable in TS.
#4CA64C	SDTM-219	LB	Record	0	LBFAST (fasting status) must use the NY codelist when present in LB.
#4CA64C	SDTM-220	LB	Record	0	LBCLSIG (clinically significant) must use the NY codelist when present in LB.
#4CA64C	SDTM-221	LB	Variable	0	LBLLOQ (lower limit of quantification) must be a numeric variable in LB.
#4CA64C	SDTM-222	TE	Record	0	TESTRL (trial element start rule) must not be null in the TE dataset.
#4CA64C	SDTM-223	TE	Variable	0	TESTRL (trial element start rule) must be present as a variable in the TE dataset.
#4CA64C	SDTM-224	TV	Record	0	TVSTRL (trial visit start rule) must not be null in the TV dataset.
#4CA64C	SDTM-225	TV	Variable	0	VISITNUM must be a numeric variable in TV.
#4CA64C	SDTM-226	TA	Variable	0	TAETORD (order of element within arm) must be a numeric variable in TA.
#4CA64C	SDTM-227	TA	Record	0	ETCD (element code) must not be null in the TA dataset.
#4CA64C	SDTM-228	IE	Record	0	DOMAIN column value must equal 'IE' in the IE dataset.
#4CA64C	SDTM-229	SV	Record	0	DOMAIN column value must equal 'SV' in the SV dataset.
#4CA64C	SDTM-230	PE	Record	0	DOMAIN column value must equal 'PE' in the PE dataset.
#4CA64C	SDTM-231	TS	Record	0	DOMAIN column value must equal 'TS' in the TS dataset.
#4CA64C	SDTM-232	TE	Record	0	DOMAIN column value must equal 'TE' in the TE dataset.
#4CA64C	SDTM-233	EG	Variable	0	EG domain must contain required result variables: EGORRES, EGSTRESC, EGSTRESN, EGSTRESU.
#4CA64C	SDTM-234	PE	Variable	0	PE domain must contain required result variables: PESTRESC, PESTRESN.
#4CA64C	SDTM-235	AE	Variable	0	AESTDTC (start date/time of adverse event) must be present as a variable in the AE dataset.
#4CA64C	SDTM-236	EX	Variable	0	EXDOSE (dose per administration) must be present as a variable in the EX dataset.
#4CA64C	SDTM-238	TV	Record	0	DOMAIN column value must equal 'TV' in the TV dataset.
#4CA64C	SDTM-239	TI	Record	0	DOMAIN column value must equal 'TI' in the TI dataset.
#4CA64C	SDTM-241	SUPPDM	Record	0	QNAM must not be null in SUPPDM.
#4CA64C	SDTM-242	SUPPDM	Record	0	QVAL must not be null in SUPPDM.
#4CA64C	SDTM-243	SUPPAE	Record	0	QNAM must not be null in SUPPAE.
#4CA64C	SDTM-244	SUPPAE	Record	0	QVAL must not be null in SUPPAE.
#4CA64C	SDTM-245	SUPPLB	Record	0	QNAM must not be null in SUPPLB.
#4CA64C	SDTM-246	SUPPDM	Record	0	RDOMAIN in SUPPDM must equal 'DM'.
#4CA64C	SDTM-247	SUPPAE	Record	0	RDOMAIN in SUPPAE must equal 'AE'.
#4CA64C	SDTM-248	SUPPLB	Record	0	RDOMAIN in SUPPLB must equal 'LB'.
#4CA64C	SDTM-249	SUPPVS	Record	0	RDOMAIN in SUPPVS must equal 'VS'.
#4CA64C	SDTM-250	SUPPVS	Record	0	QNAM must not be null in SUPPVS.
#4CA64C	SDTM-251	AE	Record	0	AESTDTC in AE must conform to ISO 8601 format when present.
#4CA64C	SDTM-255	EX	Record	0	EXSTDTC in EX must conform to ISO 8601 format when present.
#4CA64C	SDTM-258	MH	Record	0	MHENDTC in MH must conform to ISO 8601 format when present.
#4CA64C	SDTM-259	DS	Record	0	DSSTDTC in DS must conform to ISO 8601 format when present.
#4CA64C	SDTM-261	VS	Record	0	VSTESTCD (test short name) must not be null in VS.
#4CA64C	SDTM-262	LB	Record	0	LBTESTCD (test short name) must not be null in LB.
#4CA64C	SDTM-263	EG	Record	0	EGTESTCD (test short name) must not be null in EG.
#4CA64C	SDTM-264	VS	Record	0	VSTEST (test name) must not be null in VS.
#4CA64C	SDTM-265	LB	Record	0	LBTEST (test name) must not be null in LB.
#4CA64C	SDTM-268	EG	Record	0	EGORRES (original result) must not be null in EG.
#4CA64C	SDTM-269	PE	Record	0	PEORRES (original result) must not be null in PE.
#4CA64C	SDTM-270	LB	Variable	0	VISITNUM must be a numeric variable in LB.
#4CA64C	SDTM-271	AE	Record	0	AESEQ (sequence number) must not be null in AE.
#4CA64C	SDTM-272	CM	Record	0	CMSEQ (sequence number) must not be null in CM.
#4CA64C	SDTM-273	EX	Record	0	EXSEQ (sequence number) must not be null in EX.
#4CA64C	SDTM-274	MH	Record	0	MHSEQ (sequence number) must not be null in MH.
#4CA64C	SDTM-275	VS	Record	0	VSSEQ (sequence number) must not be null in VS.
#4CA64C	SDTM-276	LB	Record	0	LBSEQ (sequence number) must not be null in LB.
#4CA64C	SDTM-277	EG	Record	0	EGSEQ (sequence number) must not be null in EG.
#4CA64C	SDTM-278	PE	Record	0	PESEQ (sequence number) must not be null in PE.
#4CA64C	SDTM-279	DS	Record	0	DSSEQ (sequence number) must not be null in DS.
#4CA64C	SDTM-280	IE	Record	0	IESEQ (sequence number) must not be null in IE.
#4CA64C	SDTM-281	LB	Variable	0	LBSTNRLO (lower limit of reference range) must be a numeric variable in LB.
#4CA64C	SDTM-282	LB	Variable	0	LBSTNRHI (upper limit of reference range) must be a numeric variable in LB.
#4CA64C	SDTM-283	VS	Variable	0	VSSTRESN (numeric result in standard units) must be a numeric variable in VS.
#4CA64C	SDTM-285	VS	Record	0	VSNRIND (reference range indicator) must use values from the NRIND controlled terminology codelist when present in VS.
#4CA64C	SDTM-286	EG	Record	0	EGNRIND (reference range indicator) must use values from the NRIND controlled terminology codelist when present in EG.
#4CA64C	SDTM-287	PE	Record	0	PENRIND (reference range indicator) must use values from the NRIND controlled terminology codelist when present in PE.
#4CA64C	SDTM-288	EG	Variable	0	VISITNUM must be a numeric variable in EG.
#4CA64C	SDTM-289	AE	Variable	0	AESER (serious event flag) must be present in the AE dataset.
#4CA64C	SDTM-290	AE	Variable	0	AESEQ must be a numeric variable in AE.
#4CA64C	SDTM-291	VS	Variable	0	VSSEQ must be a numeric variable in VS.
#4CA64C	SDTM-292	LB	Variable	0	LBSEQ must be a numeric variable in LB.
#4CA64C	SDTM-293	EG	Variable	0	EGSEQ must be a numeric variable in EG.
#4CA64C	SDTM-294	CM	Variable	0	CMSEQ must be a numeric variable in CM.
#4CA64C	SDTM-295	DS	Variable	0	DSSEQ must be a numeric variable in DS.
#4CA64C	SDTM-296	EX	Variable	0	EXSEQ must be a numeric variable in EX.
#4CA64C	SDTM-298	MH	Variable	0	MHSEQ must be a numeric variable in MH.
#4CA64C	SDTM-299	SUPPVS	Record	0	QVAL must not be null in SUPPVS.
#4CA64C	SDTM-300	PE	Record	0	PETEST (physical exam test name) must not be null in PE.
#4CA64C	SDTM-301	AE	Record	0	AETERM (verbatim adverse event term) must not be null in AE.
#4CA64C	SDTM-302	AE	Record	0	AEDECOD (dictionary-derived preferred term) must not be null in AE.
#4CA64C	SDTM-304	CM	Record	0	CMTRT (verbatim medication name) must not be null in CM.
#4CA64C	SDTM-306	DS	Record	0	DSTERM (verbatim disposition term) must not be null in DS.
#4CA64C	SDTM-307	DS	Record	0	DSDECOD (dictionary-derived disposition term) must not be null in DS.
#4CA64C	SDTM-308	MH	Record	0	MHTERM (verbatim medical history term) must not be null in MH.
#4CA64C	SDTM-309	EX	Record	0	EXTRT (treatment name administered) must not be null in EX.
#4CA64C	SDTM-313	EG	Record	0	EGSTRESC (standardized result in character format) must not be null in EG.
#4CA64C	SDTM-314	PE	Record	0	PESTRESC (standardized result in character format) must not be null in PE.
#4CA64C	SDTM-315	VS	Variable	0	VSSTRESU column must be present in VS.
#4CA64C	SDTM-316	LB	Variable	0	LBSTRESU column must be present in LB.
#4CA64C	SDTM-317	EG	Variable	0	EGSTRESU column must be present in EG.
#4CA64C	SDTM-318	LB	Variable	0	LBSTRESN must be numeric type in LB.
#4CA64C	SDTM-319	EG	Variable	0	EGSTRESN must be numeric type in EG.
#4CA64C	SDTM-320	EX	Variable	0	EXDOSE must be numeric type in EX.
#4CA64C	SDTM-321	TA	Record	0	ARM (arm name) must not be null in TA.
#4CA64C	SDTM-322	TA	Record	0	EPOCH (epoch name) must not be null in TA.
#4CA64C	SDTM-328	TI	Record	0	IETEST (inclusion/exclusion criterion name) must not be null in TI.
#4CA64C	SDTM-329	TI	Record	0	IETESTCD (criterion short name) must not be null in TI.
#4CA64C	SDTM-330	TS	Record	0	TSPARMCD (trial summary parameter short name) must not be null in TS.
#4CA64C	SDTM-331	SV	Record	0	VISITNUM must not be null in SV.
#4CA64C	SDTM-333	SV	Record	0	SVSTDTC must conform to ISO 8601 date/time format when present in SV.
#4CA64C	SDTM-334	SV	Record	0	SVENDTC must conform to ISO 8601 date/time format when present in SV.
#4CA64C	SDTM-337	EG	Record	0	VISITNUM must not be null in EG.
#4CA64C	SDTM-338	PE	Record	0	VISITNUM must not be null in PE.
#4CA64C	SDTM-339	VS	Variable	0	VISITNUM must be numeric type in VS.
#4CA64C	SDTM-340	PE	Variable	0	VISITNUM must be numeric type in PE.
#4CA64C	SDTM-341	SUPPDM	Record	0	QLABEL must not be null in SUPPDM.
#4CA64C	SDTM-342	SUPPAE	Record	0	QLABEL must not be null in SUPPAE.
#4CA64C	SDTM-343	SUPPLB	Record	0	QLABEL must not be null in SUPPLB.
#4CA64C	SDTM-344	SUPPEG	Record	0	QNAM must not be null in SUPPEG.
#4CA64C	SDTM-345	SUPPEG	Record	0	QVAL must not be null in SUPPEG.
#4CA64C	SDTM-346	SUPPEG	Record	0	RDOMAIN must equal 'EG' in the SUPPEG dataset.
#4CA64C	SDTM-347	SUPPCM	Record	0	QNAM must not be null in SUPPCM.
#4CA64C	SDTM-348	SUPPCM	Record	0	RDOMAIN must equal 'CM' in the SUPPCM dataset.
#4CA64C	SDTM-349	SUPPDS	Record	0	QNAM must not be null in SUPPDS.
#4CA64C	SDTM-350	SUPPDS	Record	0	RDOMAIN must equal 'DS' in the SUPPDS dataset.
#4CA64C	SDTM-351	SUPPMH	Record	0	QNAM must not be null in SUPPMH.
#4CA64C	SDTM-352	SUPPMH	Record	0	RDOMAIN must equal 'MH' in the SUPPMH dataset.
#4CA64C	SDTM-353	AE	Variable	0	AESTDY (AE start study day) must be numeric type in AE.
#4CA64C	SDTM-354	AE	Variable	0	AEENDY (AE end study day) must be numeric type in AE.
#4CA64C	SDTM-355	CM	Variable	0	CMSTDY (CM start study day) must be numeric type in CM.
#4CA64C	SDTM-356	EX	Variable	0	EXSTDY (EX start study day) must be numeric type in EX.
#4CA64C	SDTM-357	MH	Variable	0	MHSTDY (MH start study day) must be numeric type in MH.
#4CA64C	SDTM-358	VS	Variable	0	VSSTDY (VS study day) must be numeric type in VS.
#4CA64C	SDTM-359	LB	Variable	0	LBSTDY (LB study day) must be numeric type in LB.
#4CA64C	SDTM-360	EG	Variable	0	EGSTDY (EG study day) must be numeric type in EG.
#4CA64C	SDTM-361	VS	Record	0	VSDTC in VS must conform to ISO 8601 format when present.
#4CA64C	SDTM-362	LB	Record	0	LBDTC in LB must conform to ISO 8601 format when present.
#4CA64C	SDTM-363	EG	Record	0	EGDTC in EG must conform to ISO 8601 format when present.
#4CA64C	SDTM-364	PE	Record	0	PEDTC in PE must conform to ISO 8601 format when present.
#4CA64C	SDTM-365	AE	Record	0	AEDTC in AE must conform to ISO 8601 format when present.
#4CA64C	SDTM-366	DM	Record	0	BRTHDTC in DM must conform to ISO 8601 format when present.
#4CA64C	SDTM-368	EX	Variable	0	EXENDY in EX must be a numeric type variable.
#4CA64C	SDTM-369	CM	Variable	0	CMENDY in CM must be a numeric type variable.
#4CA64C	SDTM-370	MH	Variable	0	MHENDY in MH must be a numeric type variable.
#4CA64C	SDTM-374	EX	Variable	0	EXDOSFRM (dose form) column must be present in the EX domain.
#4CA64C	SDTM-377	VS	Variable	0	VSDY in VS must be a numeric type variable.
#4CA64C	SDTM-378	LB	Variable	0	LBDY in LB must be a numeric type variable.
#4CA64C	SDTM-379	EG	Variable	0	EGDY in EG must be a numeric type variable.
#4CA64C	SDTM-380	PE	Variable	0	PEDY in PE must be a numeric type variable.
#4CA64C	SDTM-381	SUPPVS	Record	0	QLABEL must not be null in SUPPVS; the qualifier label is required in all supplemental datasets.
#4CA64C	SDTM-382	SUPPEG	Record	0	QLABEL must not be null in SUPPEG; the qualifier label is required in all supplemental datasets.
#4CA64C	SDTM-383	SUPPCM	Record	0	QLABEL must not be null in SUPPCM; the qualifier label is required in all supplemental datasets.
#4CA64C	SDTM-384	SUPPDS	Record	0	QLABEL must not be null in SUPPDS; the qualifier label is required in all supplemental datasets.
#4CA64C	SDTM-385	SUPPMH	Record	0	QLABEL must not be null in SUPPMH; the qualifier label is required in all supplemental datasets.
#4CA64C	SDTM-394	TS	Record	0	TSPARM must not be null in TS; the parameter name is required for every trial summary record.
#4CA64C	SDTM-395	TI	Record	0	IECAT must not be null in TI; the inclusion/exclusion category is required for each criterion record.
#4CA64C	SDTM-396	TA	Record	0	ARMCD must not be null in TA; the arm code is required for every trial arm record.
#4CA64C	SDTM-397	TA	Record	0	TAETORD must not be null in TA; the planned element order within the arm is required.
#4CA64C	SDTM-407	PE	Variable	0	PESEQ in PE must be a numeric type variable.
#4CA64C	SDTM-408	IE	Variable	0	IESEQ in IE must be a numeric type variable.
#4CA64C	SDTM-409	PE	Variable	0	PEENDY in PE must be a numeric type variable.
#4CA64C	SDTM-410	IE	Variable	0	IESTDY in IE must be a numeric type variable.
#4CA64C	SDTM-411	IE	Variable	0	IEENDY in IE must be a numeric type variable.
#4CA64C	SDTM-412	DS	Variable	0	DSENDY in DS must be a numeric type variable.
#4CA64C	SDTM-421	SUPPDM	Record	0	DOMAIN must equal 'SUPPDM' in the SUPPDM dataset; each record must carry the correct supplemental qualifier domain code.
#4CA64C	SDTM-422	SUPPAE	Record	0	DOMAIN must equal 'SUPPAE' in the SUPPAE dataset; each record must carry the correct supplemental qualifier domain code.
#4CA64C	SDTM-423	SUPPLB	Record	0	DOMAIN must equal 'SUPPLB' in the SUPPLB dataset; each record must carry the correct supplemental qualifier domain code.
#4CA64C	SDTM-424	SUPPVS	Record	0	DOMAIN must equal 'SUPPVS' in the SUPPVS dataset; each record must carry the correct supplemental qualifier domain code.
#4CA64C	SDTM-425	SUPPEG	Record	0	DOMAIN must equal 'SUPPEG' in the SUPPEG dataset; each record must carry the correct supplemental qualifier domain code.
#4CA64C	SDTM-426	SUPPCM	Record	0	DOMAIN must equal 'SUPPCM' in the SUPPCM dataset; each record must carry the correct supplemental qualifier domain code.
#4CA64C	SDTM-427	SUPPDS	Record	0	DOMAIN must equal 'SUPPDS' in the SUPPDS dataset; each record must carry the correct supplemental qualifier domain code.
#4CA64C	SDTM-428	SUPPMH	Record	0	DOMAIN must equal 'SUPPMH' in the SUPPMH dataset; each record must carry the correct supplemental qualifier domain code.
#4CA64C	SDTM-432	EX	Record	0	EXROUTE in EX must be a valid ROUTE codelist term when present.
#4CA64C	SDTM-433	EX	Record	0	EXDOSFRM in EX must be a valid FRM codelist term when present.
#4CA64C	SDTM-434	TA	Record	0	EPOCH in TA must be a valid EPOCH codelist term when present.
#AAAAAA	SDTM-049	DEFINE	Variable	0	Variable labels in the submission must match the labels specified in Define-XML.
#AAAAAA	SDTM-050	DEFINE	Variable	0	Variable data types in the submission must match the types declared in Define-XML.
#AAAAAA	SDTM-051	DEFINE	Define	0	Each variable in the DM dataset must be declared in Define-XML.
#AAAAAA	SDTM-052	DEFINE	Define	0	Each variable in the AE dataset must be declared in Define-XML.
#AAAAAA	SDTM-053	DEFINE	Define	0	Variables declared Mandatory in Define-XML must not contain null values in DM.
#AAAAAA	SDTM-054	DEFINE	Define	0	Variables declared Mandatory in Define-XML must not contain null values in AE.
#AAAAAA	SDTM-055	DEFINE	Define	0	Variable data types in DM must match the types declared in Define-XML.
#AAAAAA	SDTM-056	DEFINE	Codelist	0	SEX values in DM must be from the codelist declared in Define-XML.
#AAAAAA	SDTM-057	DEFINE	Codelist	0	RACE values in DM must be from the codelist declared in Define-XML.
#AAAAAA	SDTM-058	DEFINE	Codelist	0	ETHNIC values in DM must be from the codelist declared in Define-XML.
#AAAAAA	SDTM-059	DEFINE	Codelist	0	AESEV values in AE must be from the codelist declared in Define-XML.
#AAAAAA	SDTM-060	DEFINE	Codelist	0	AEOUT values in AE must be from the codelist declared in Define-XML.
CT: sdtm-ct-2024-09-27

The ten failing rules are at the top, followed by the passing ones. The table has all 362 rules, so it’s long. Displaying report itself in a notebook gives the same table.

get_findings_table()

Make a table of the failing records found by the built-in rules.

Usage

Source

get_findings_table()

The table of get_tabular_report() has a row for each rule, with its number of issues. The findings table goes into more detail, with a row for each failing record, so that a reviewer can see which subjects and values have to be fixed. It has the same findings as findings_df(), with at most 100 for each rule.

The columns of the table are in two groups. Under SDTM Rule Definition are the rule’s identifier, its domain and its description. Under Finding are the record’s USUBJID, the variable that was checked, the record’s row number (counting from 1) and the value that failed. The subtitle has the standard and its version and the numbers of rules that failed, passed and were not applicable.

Returns

GT

The table, as a Great Tables GT object.

Raises

TypeError

If the report isn’t of the built-in rules.

ValueError

If there are no failing records. This happens when no rule that checks values failed, even if other rules (such as those for missing variables) did.

Examples

Let’s check a small DM dataset with the built-in rules and make a table of its failing records:

import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "RFSTDTC": ["2024-01-15", "15JAN2024", "2024-01-17"],
        "AGE": [34, 51, -38],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

report.get_findings_table()

/Users/riannone/py_projects/pointblank/.venv/lib/python3.11/site-packages/great_tables/_render_checks.py:37: RenderWarning: Rendering table with .cols_width() in Quarto may result in unexpected behavior. This is because Quarto performs custom table processing. Either use all percentage widths, or set .tab_options(quarto_disable_processing=True) to disable Quarto table processing.
  warnings.warn(

Findings Report for CDISC Conformance
SDTMIG 3-4 · 10 failed (340 passed) · 12 n/a
	SDTM Rule Definition			Finding
	Rule	Domain	Description	USUBJID	Column	Row	Value
#FF3300	SDTM-007	DM	SEX in DM must use values from the CDISC controlled terminology codelist SEX.	STUDY01-003	SEX	3	FEMALE
#FF3300	SDTM-011	DM	RFSTDTC in DM must be in ISO 8601 extended datetime format when present.	STUDY01-002	RFSTDTC	2	15JAN2024
#FF3300	SDTM-195	DM	AGE must not be negative in the DM dataset.	STUDY01-003	AGE	3	-38

The third subject’s record has two problems, a negative AGE and a SEX of "FEMALE", so it’s in the table twice. The second subject’s RFSTDTC isn’t in the ISO 8601 format.

rules()

Get the result of each rule.

Usage

Source

rules(status=None)

Every rule that was run has a result, whether it passed or not. The rules() method gets these results, optionally only those with a particular status. This is useful for listing the failing rules, or for finding out why some rules were not applicable.

Parameters

status: str | None = None

Only keep the rules with this status. For the built-in rules, this can be "pass", "fail", "error", "not_applicable" or "not_supported". For CDISC CORE, it is one of CORE’s statuses, such as "SUCCESS", "ISSUE REPORTED", "SKIPPED" or "EXECUTION ERROR". The default is None, which keeps all the rules.

Returns

list

For a report of the built-in rules, a NativeRuleResult for each rule, with the attributes rule_id, rule_type, dataset, status, sensitivity, description, message, n_issues and row_findings (its findings, as returned by findings()).

For a CDISC CORE report, a CoreRuleResult for each rule, with the attributes rule_id, status, message, version, cdisc_rule_id and fda_rule_id.

For a Validate-based report, an empty list.

Examples

Let’s check a small DM dataset with the built-in rules and look at the rules that are not applicable:

import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

not_applicable = report.rules(status="not_applicable")

print(len(not_applicable))
print({rule.message for rule in not_applicable})

12
{'Required input(s) not provided: DEFINE'}

The 12 rules all check the datasets against a Define-XML, which wasn’t given. With the define_xml= argument of validate_sdtmig(), they would be run too.

issues()

List the issues that the conformance check found.

Usage

Source

issues(
    severity=None,
    status=None,
)

An issue is a rule (or, for a Validate-based report, a validation step) that found something wrong. The list is a quick way to see what has to be fixed, and it can be turned into a DataFrame or saved. Each issue is a dict, whose keys depend on the form of the report.

Parameters

severity: str | None = None

For a Validate-based report, only keep the issues of this severity: "warning", "error" or "critical". The severity of a step comes from the thresholds= of validate_conformance(), so without thresholds no issue has a severity. This is ignored for the other forms of report. The default is None, which keeps all the issues.

status: str | None = None

For a CDISC CORE report, only keep the issues of rules with this status, such as "ISSUE REPORTED". This is ignored for the other forms of report. The default is None, which keeps all the issues.

Returns

list[dict]

For a report of the built-in rules, a dict for each rule with issues, with the keys dataset, rule_id, rule_type, message, n_issues, sensitivity ("Error" or "Warning") and status.

For a CDISC CORE report, a dict for each rule and dataset with issues, with the keys dataset, rule_id, message, issues (the number of issues) and status.

For a Validate-based report, a dict for each step with failing test units, with the keys dataset, step, assertion, column, n_failed, n and severity.

Examples

Let’s check a small DM dataset with the built-in rules and list the issues as a DataFrame:

import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "RFSTDTC": ["2024-01-15", "15JAN2024", "2024-01-17"],
        "AGE": [34, 51, -38],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

pl.DataFrame(report.issues()).select("rule_id", "dataset", "sensitivity", "n_issues")

shape: (10, 4)

rule_id	dataset	sensitivity	n_issues
str	str	str	i64
"SDTM-007"	"DM"	"Error"	1
"SDTM-011"	"DM"	"Error"	1
"SDTM-031"	"DM"	"Error"	1
"SDTM-032"	"DM"	"Error"	1
"SDTM-033"	"DM"	"Error"	1
"SDTM-034"	"DM"	"Error"	1
"SDTM-035"	"DM"	"Error"	1
"SDTM-121"	"TS"	"Error"	1
"SDTM-122"	"TA"	"Warning"	1
"SDTM-195"	"DM"	"Error"	1

The missing TS and TA datasets are given as the datasets of SDTM-121 and SDTM-122. Only SDTM-122 is of "Warning" sensitivity. A Validate-based report gives the severity of each step instead, when thresholds are set:

ae = pl.DataFrame({"USUBJID": ["STUDY01-001", "STUDY01-009"], "AESEQ": [1, 1]})
study = pb.SubmissionPackage(datasets={"DM": dm, "AE": ae})

report = study.validate_conformance(engine="validate", thresholds=pb.Thresholds(warning=1))

report.issues(severity="warning")

[{'dataset': 'AE',
  'step': 5,
  'assertion': 'specially',
  'column': None,
  'n_failed': 1,
  'n': 2,
  'severity': 'warning'},
 {'dataset': 'DM',
  'step': 10,
  'assertion': 'col_vals_expr',
  'column': None,
  'n_failed': 1,
  'n': 3,
  'severity': 'warning'},
 {'dataset': 'DM',
  'step': 11,
  'assertion': 'col_vals_regex',
  'column': 'RFSTDTC',
  'n_failed': 1,
  'n': 3,
  'severity': 'warning'}]

The issues are the orphan subject in AE, and the length of "FEMALE" and the date "15JAN2024" in DM. Each of them is one failing test unit, which reaches the warning threshold.

findings()

Get the failing records that the conformance check found.

Usage

Source

findings()

A rule that checks the values of a dataset (such as a codelist check on SEX) fails for particular records, and each of these is a finding. The findings() method gets them all as a list of objects. For the built-in rules, findings_df() gives the same findings as a DataFrame, which is often easier to work with.

Returns

list

For a report of the built-in rules, a NativeRowFinding for each failing record, with the attributes rule_id, dataset, row (the 0-based position of the record), usubjid, checked_column, checked_value, context (the record’s identifying variables, such as STUDYID) and message. At most 100 findings are kept for each rule.

For a CDISC CORE report, a CoreFinding for each entry of CORE’s Issue_Details, with the attributes rule_id, message, dataset, executability, usubjid, row (the 1-based position of the record), seq, variables and values.

For a Validate-based report, an empty list.

Examples

Let’s check a small DM dataset with the built-in rules:

import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "RFSTDTC": ["2024-01-15", "15JAN2024", "2024-01-17"],
        "AGE": [34, 51, -38],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

for finding in report.findings():
    print(finding.rule_id, finding.row, finding.usubjid, finding.message)

SDTM-007 2 STUDY01-003 SEX value is not in the SEX codelist.
SDTM-011 1 STUDY01-002 RFSTDTC does not conform to ISO 8601 extended datetime format.
SDTM-195 2 STUDY01-003 AGE must not be negative in DM.

Ten rules fail, but only these three check values. The others fail because a variable or a dataset is missing, so they have no failing records.

findings_df()

Get the failing records found by the built-in rules, as a DataFrame.

Usage

Source

findings_df()

Each row of the DataFrame is a record that failed a rule. As a DataFrame, the findings are easy to filter by rule, count by subject, save as a CSV file, or join back to the datasets to look at the records in full. The rules that check values (record checks, dataset contents checks and checks against the Define-XML’s codelists) give findings. Rules that check whether variables or datasets are present have a number of issues but no findings. At most 100 findings are kept for each rule, though the rule’s number of issues (as in issues()) counts all of its failing records.

Returns

polars.DataFrame

A DataFrame with a row for each finding and these columns:

• rule_id: the identifier of the rule, such as "SDTM-007"
• dataset: the dataset of the record, such as "DM"
• row_index: the 0-based position of the record in its dataset
• usubjid: the record’s USUBJID, or "" if it has none
• checked_column: the variable that was checked, such as "SEX"
• checked_value: the value that failed, as a string
• description: the description of the rule

If there are no findings, the DataFrame is empty but has the same columns.

Raises

TypeError

If the report isn’t of the built-in rules. For a CDISC CORE report, use findings() instead.

Examples

Let’s check a small DM dataset with the built-in rules and get its failing records:

import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "RFSTDTC": ["2024-01-15", "15JAN2024", "2024-01-17"],
        "AGE": [34, 51, -38],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

findings = report.findings_df()

findings.drop("description")

shape: (3, 6)

rule_id	dataset	row_index	usubjid	checked_column	checked_value
str	str	i64	str	str	str
"SDTM-007"	"DM"	2	"STUDY01-003"	"SEX"	"FEMALE"
"SDTM-011"	"DM"	1	"STUDY01-002"	"RFSTDTC"	"15JAN2024"
"SDTM-195"	"DM"	2	"STUDY01-003"	"AGE"	"-38"

Since row_index is 0-based, it can be used to get the failing records from the dataset:

dm[findings["row_index"].unique().sort().to_list()]

shape: (2, 6)

STUDYID	DOMAIN	USUBJID	RFSTDTC	AGE	SEX
str	str	str	str	i64	str
"STUDY01"	"DM"	"STUDY01-002"	"15JAN2024"	51	"F"
"STUDY01"	"DM"	"STUDY01-003"	"2024-01-17"	-38	"FEMALE"

The second and third records have problems, and the first has none.

get_validation()

Get the validation of one dataset from a Validate-based report.

Usage

Source

get_validation(name)

The "validate" engine of the SubmissionPackage method validate_conformance() makes a Validate validation for each dataset. The get_validation() method gets one of them, so that its validation report or its failing rows can be looked at. Indexing the report (as in report["AE"]) does the same, but get_validation() gives a more helpful error for a name that isn’t in the report. Reports of the built-in rules and of CDISC CORE don’t have validations.

Parameters

name: str

The name of the dataset, such as "AE". This is case-insensitive.

Returns

Validate

The validation of the dataset.

Raises

KeyError

If the report has no validation for that dataset. The error message lists the datasets that it has validations for.

Examples

Let’s check a package where the AE dataset has a subject, STUDY01-009, who isn’t in DM:

import pointblank as pb
import polars as pl

dm = pl.DataFrame({"USUBJID": ["STUDY01-001", "STUDY01-002"], "DOMAIN": ["DM", "DM"]})
ae = pl.DataFrame(
    {
        "USUBJID": ["STUDY01-001", "STUDY01-009"],
        "DOMAIN": ["AE", "AE"],
        "AESEQ": [1, 1],
    }
)

study = pb.SubmissionPackage(datasets={"DM": dm, "AE": ae})
report = study.validate_conformance(engine="validate")

report.get_validation("ae")

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
#4CA64C	1	col_vals_not_null()	DOMAIN	—		✓	2	2 1.00	0 0.00	—	—	—	—
#4CA64C	2	col_vals_not_null()	USUBJID	—		✓	2	2 1.00	0 0.00	—	—	—	—
#4CA64C	3	col_vals_not_null()	AESEQ	—		✓	2	2 1.00	0 0.00	—	—	—	—
#4CA64C	4	col_vals_in_set()	DOMAIN	AE		✓	2	2 1.00	0 0.00	—	—	—	—
#4CA64C	5	col_vals_gt()	AESEQ	0		✓	2	2 1.00	0 0.00	—	—	—	—
#4CA64C	6	col_vals_expr() DOMAIN length <= 2	DOMAIN	COLUMN EXPR		✓	2	2 1.00	0 0.00	—	—	—	—
#4CA64C	7	col_vals_expr() USUBJID length <= 40	USUBJID	COLUMN EXPR		✓	2	2 1.00	0 0.00	—	—	—	—
#4CA64C66	8	specially() USUBJID values exist in DM (AE → DM)		EXPR		✓	2	1 0.50	1 0.50	—	—	—	—

The last step checks that each USUBJID in AE is in DM, and it has one failing test unit. The validation is an ordinary Validate object, so methods such as get_step_report() can be used on it to see the failing record.

to_json()

Save the report as a JSON file.

Usage

Source

to_json(path)

A JSON file keeps the results of a conformance check for later, such as for comparing the results of two runs or for passing them to another tool. For a report of the built-in rules or a Validate-based report, the file has two entries: summary, as from summary(), and issues, as from issues(). For a CDISC CORE report, the file has the five sections of CORE’s own JSON report, so it can be read by tools that read CORE reports, and back into a report with from_core_report().

Parameters

path: str | Path

The path of the file to write, including its name. Directories in the path that don’t exist are created.

Returns

Path

The path of the file that was written.

Examples

Let’s check a small DM dataset with the built-in rules and save the report in a temporary directory:

import json
import tempfile
from pathlib import Path

import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

path = report.to_json(Path(tempfile.mkdtemp()) / "conformance" / "dm.json")

saved = json.loads(path.read_text())

print(saved["summary"]["status_counts"])
print(saved["issues"][0])

{'pass': 342, 'fail': 8, 'not_applicable': 12}
{'dataset': 'DM', 'rule_id': 'SDTM-007', 'rule_type': 'RECORD_CHECK', 'message': 'SEX value is not in the SEX codelist.', 'n_issues': 1, 'sensitivity': 'Error', 'status': 'fail'}

The conformance directory didn’t exist, so it was created. Each issue is saved with all its keys, as issues() gives it.

to_excel()

Save the report as an Excel workbook.

Usage

Source

to_excel(path)

A workbook is a convenient way to share the results of a conformance check with people who review them in a spreadsheet. The sheets depend on the form of the report:

• For the built-in rules, Rules_Report has a row for each rule, with its type, dataset, status, sensitivity, number of issues and message. Issues has the rules with issues (as from issues()), and Summary has the entries of summary().
• For CDISC CORE, the sheets are Issue_Summary, Issue_Details, Rules_Report and Conformance_Details, like those of CORE’s own Excel report.
• For a Validate-based report, Issues has the failing steps and Summary has a row for each dataset.

A sheet that would be empty, such as Issues when there are no issues, is left out. The openpyxl and pandas packages are needed for writing the workbook.

Parameters

path: str | Path

The path of the file to write, including its name (which usually ends in .xlsx). Directories in the path that don’t exist are created.

Returns

Path

The path of the file that was written.

Raises

ImportError

If openpyxl or pandas isn’t installed.

Examples

Let’s check a small DM dataset with the built-in rules and save the report in a temporary directory:

import tempfile
from pathlib import Path

import pandas as pd
import pointblank as pb
import polars as pl

dm = pl.DataFrame(
    {
        "STUDYID": ["STUDY01"] * 3,
        "DOMAIN": ["DM"] * 3,
        "USUBJID": ["STUDY01-001", "STUDY01-002", "STUDY01-003"],
        "SEX": ["M", "F", "FEMALE"],
    }
)

report = pb.validate_sdtmig({"DM": dm})

path = report.to_excel(Path(tempfile.mkdtemp()) / "dm_conformance.xlsx")

pd.ExcelFile(path).sheet_names

['Rules_Report', 'Issues', 'Summary']

Here’s the start of the Rules_Report sheet, whose rows are in the order of the rules catalog:

pd.read_excel(path, sheet_name="Rules_Report").head(3)

	Rule ID	Rule Type	Dataset	Status	Sensitivity	Issues	Message
0	SDTM-001	DOMAIN_PRESENCE_CHECK	DM	pass	Error	0	DM (Demographics) domain is required in every ...
1	SDTM-002	RECORD_CHECK	DM	pass	Error	0	STUDYID must not be null in any domain.
2	SDTM-003	RECORD_CHECK	DM	pass	Error	0	DOMAIN must not be null in any SDTM dataset.

The workbook has a row for each of the 362 rules, so the passing rules are recorded as well as the failing ones.

from_core_report()

Make a report from the JSON report of a CDISC CORE run.

Usage

Source

from_core_report(
    report,
    package=None,
    agency=None,
)

CDISC CORE can be run on its own (as core validate ... -of JSON), and its JSON report can be kept for later. The from_core_report() class method reads such a report into a ConformanceReport, which gives it the same methods as the report of validate_cdisc_submission(). The JSON report has five sections: Conformance_Details (the standard, its version and the version of CORE), Dataset_Details, Issue_Summary (the number of issues for each rule and dataset), Issue_Details (one entry for each failing record) and Rules_Report (the status of each rule). A file written by to_json() from a CORE report has the same sections, so it can be read back with this method.

Parameters

report: dict | ParsedCoreReport

The CORE report, as a dict (such as from json.load()) or as the already parsed report of an earlier ConformanceReport (its core attribute).

package: SubmissionPackage | None = None

The SubmissionPackage that CORE checked. The default is None.

agency: str | None = None

The regulatory agency, such as "FDA", which is only used as a label in the report. The default is None.

Returns

ConformanceReport

A report of CORE’s results, whose is_core is True.

Examples

Let’s write a short report by hand in CORE’s format, for three rules on a DM and an AE dataset. One rule found an AE record whose subject isn’t in DM, one rule passed and one was skipped:

import pointblank as pb

core_json = {
    "Conformance_Details": {
        "CORE_Engine_Version": "0.16.0",
        "Standard": "SDTMIG",
        "Version": "V3.4",
    },
    "Dataset_Details": [
        {"filename": "dm.xpt", "label": "Demographics", "length": 3},
        {"filename": "ae.xpt", "label": "Adverse Events", "length": 2},
    ],
    "Issue_Summary": [
        {
            "dataset": "ae.xpt",
            "core_id": "CORE-000201",
            "message": "USUBJID is not found in DM.USUBJID",
            "issues": 1,
        }
    ],
    "Issue_Details": [
        {
            "core_id": "CORE-000201",
            "message": "USUBJID is not found in DM.USUBJID",
            "dataset": "ae.xpt",
            "USUBJID": "STUDY01-009",
            "row": 2,
            "SEQ": 1,
            "variables": ["USUBJID"],
            "values": ["STUDY01-009"],
        }
    ],
    "Rules_Report": [
        {"core_id": "CORE-000201", "status": "ISSUE REPORTED"},
        {"core_id": "CORE-000351", "status": "SUCCESS"},
        {"core_id": "CORE-000087", "status": "SKIPPED"},
    ],
}

report = pb.ConformanceReport.from_core_report(core_json, agency="FDA")

print(report)

ConformanceReport (CORE)
  Agency: FDA
  SDTMIG V3.4 — CORE 0.16.0
  3 rules (ISSUE REPORTED=1, SKIPPED=1, SUCCESS=1)
  1 issues — FAIL

The rule statuses are CORE’s own. A real report would usually be read from a file, with json.load(). The failing record is in findings():

report.findings()

[CoreFinding(rule_id='CORE-000201', message='USUBJID is not found in DM.USUBJID', dataset='ae.xpt', executability=None, usubjid='STUDY01-009', row=2, seq=1, variables=['USUBJID'], values=['STUDY01-009'])]

CORE gives the row of a record as a 1-based number, so row=2 is the second record of the AE dataset.