# ConformanceReport

The results of a CDISC conformance check.

Usage

[Source](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L1336-L2967)

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

A [ConformanceReport](../reference/ConformanceReport.md#pointblank.ConformanceReport) holds the results of checking a set of CDISC datasets for conformance to their standard. It is returned by [`validate_sdtmig()`](../reference/validate_sdtmig.md#pointblank.validate_sdtmig), [`validate_cdisc_submission()`](../reference/validate_cdisc_submission.md#pointblank.validate_cdisc_submission) and the [SubmissionPackage](../reference/SubmissionPackage.md#pointblank.SubmissionPackage) method [`validate_conformance()`](../reference/SubmissionPackage.md#pointblank.SubmissionPackage.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`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.findings_df) and [`get_findings_table()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.get_findings_table).
- **CDISC CORE** ([`is_core`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.from_core_report).
- **Validate-based**: a [`Validate`](../reference/Validate.md#pointblank.Validate) validation for each dataset, from `SubmissionPackage.validate_conformance(engine="validate")`. Each validation can be had with [`get_validation()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.get_validation).

The methods [`summary()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.summary), [`issues()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.issues) and [`all_passed()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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`](../reference/SubmissionPackage.md#pointblank.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:

``` python
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:

``` python
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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.get_tabular_report).

## See Also

[validate_sdtmig()](../reference/validate_sdtmig.md#pointblank.validate_sdtmig)  
Check SDTM datasets with the built-in rules.

[validate_cdisc_submission()](../reference/validate_cdisc_submission.md#pointblank.validate_cdisc_submission)  
Check a folder of datasets with CDISC CORE.

[SubmissionPackage](../reference/SubmissionPackage.md#pointblank.SubmissionPackage)  
A set of datasets to check together.

## Attributes

| Name | Description |
|----|----|
| [n_datasets](#n_datasets) | The number of datasets that were checked. |
| [is_core](#is_core) | Whether the report holds the results of CDISC CORE. |
| [is_rules](#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:

``` python
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()`](../reference/validate_cdisc_submission.md#pointblank.validate_cdisc_submission), of `SubmissionPackage.validate_conformance(engine="core")` and of [`from_core_report()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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:

``` python
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](../reference/ConformanceReport.md#pointblank.ConformanceReport.is_core) is `False` and [`is_rules`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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()`](../reference/validate_sdtmig.md#pointblank.validate_sdtmig) and of [SubmissionPackage.validate_conformance()](../reference/SubmissionPackage.md#pointblank.SubmissionPackage.validate_conformance) with its default engine (when the package's standard has a bundled rules catalog). Only these reports have [`findings_df()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.findings_df), [`get_findings_table()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.get_findings_table) and [`get_tabular_report()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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:

``` python
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`](../reference/Validate.md#pointblank.Validate) validations, so its [is_rules](../reference/ConformanceReport.md#pointblank.ConformanceReport.is_rules) is `False`, as is its [`is_core`](../reference/ConformanceReport.md#pointblank.ConformanceReport.is_core).

## Methods

| Name | Description |
|----|----|
| [all_passed()](#all_passed) | Check whether the datasets conform, with nothing failing. |
| [summary()](#summary) | Summarize the results of the conformance check as a dict. |
| [get_tabular_report()](#get_tabular_report) | Make a table of the results of the built-in rules. |
| [get_findings_table()](#get_findings_table) | Make a table of the failing records found by the built-in rules. |
| [rules()](#rules) | Get the result of each rule. |
| [issues()](#issues) | List the issues that the conformance check found. |
| [findings()](#findings) | Get the failing records that the conformance check found. |
| [findings_df()](#findings_df) | Get the failing records found by the built-in rules, as a DataFrame. |
| [get_validation()](#get_validation) | Get the validation of one dataset from a Validate-based report. |
| [to_json()](#to_json) | Save the report as a JSON file. |
| [to_excel()](#to_excel) | Save the report as an Excel workbook. |
| [from_core_report()](#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](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L1608-L1649)

``` python
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:

``` python
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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.rules), as in `report.rules(status="fail")`.

### summary()

Summarize the results of the conformance check as a dict.

Usage

[Source](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L1722-L1813)

``` python
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](../reference/Comparison.status_counts.md#pointblank.Comparison.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](../reference/Comparison.status_counts.md#pointblank.Comparison.status_counts), `n_issues`, [n_datasets](../reference/ConformanceReport.md#pointblank.ConformanceReport.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:

``` python
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](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L2659-L2885)

``` python
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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.get_findings_table) or [`findings_df()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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](https://posit-dev.github.io/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:

``` python
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](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L2487-L2657)

``` python
get_findings_table()
```

The table of [`get_tabular_report()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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](https://posit-dev.github.io/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:

``` python
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](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L1999-L2063)

``` python
rules(status=None)
```

Every rule that was run has a result, whether it passed or not. The [rules()](../reference/ConformanceReport.md#pointblank.ConformanceReport.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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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:

``` python
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()`](../reference/validate_sdtmig.md#pointblank.validate_sdtmig), they would be run too.

### issues()

List the issues that the conformance check found.

Usage

[Source](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L1815-L1939)

``` python
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()`](../reference/SubmissionPackage.md#pointblank.SubmissionPackage.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](../reference/ConformanceReport.md#pointblank.ConformanceReport.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:

``` python
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:

``` python
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](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L1941-L1997)

``` python
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()](../reference/ConformanceReport.md#pointblank.ConformanceReport.findings) method gets them all as a list of objects. For the built-in rules, [`findings_df()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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](../reference/MetadataPackage.md#pointblank.MetadataPackage.values).

For a Validate-based report, an empty list.

#### Examples

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

``` python
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](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L2383-L2485)

``` python
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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.findings) instead.

#### Examples

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

``` python
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:

``` python
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](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L1654-L1720)

``` python
get_validation(name)
```

The `"validate"` engine of the [SubmissionPackage](../reference/SubmissionPackage.md#pointblank.SubmissionPackage) method [`validate_conformance()`](../reference/SubmissionPackage.md#pointblank.SubmissionPackage.validate_conformance) makes a [`Validate`](../reference/Validate.md#pointblank.Validate) validation for each dataset. The [get_validation()](../reference/ConformanceReport.md#pointblank.ConformanceReport.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()](../reference/ConformanceReport.md#pointblank.ConformanceReport.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:

``` python
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")
```

[TABLE]

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](../reference/Validate.md#pointblank.Validate) object, so methods such as [`get_step_report()`](../reference/Validate.get_step_report.md#pointblank.Validate.get_step_report) can be used on it to see the failing record.

### to_json()

Save the report as a JSON file.

Usage

[Source](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L2100-L2208)

``` python
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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.summary), and [issues](../reference/ConformanceReport.md#pointblank.ConformanceReport.issues), as from [`issues()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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:

``` python
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()](../reference/ConformanceReport.md#pointblank.ConformanceReport.issues) gives it.

### to_excel()

Save the report as an Excel workbook.

Usage

[Source](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L2210-L2381)

``` python
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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.issues)), and `Summary` has the entries of [`summary()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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:

``` python
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:

``` python
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](https://github.com/posit-dev/pointblank/blob/main/pointblank/metadata/_submission.py#L1439-L1542)

``` python
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()](../reference/ConformanceReport.md#pointblank.ConformanceReport.from_core_report) class method reads such a report into a [ConformanceReport](../reference/ConformanceReport.md#pointblank.ConformanceReport), which gives it the same methods as the report of [`validate_cdisc_submission()`](../reference/validate_cdisc_submission.md#pointblank.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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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](../reference/ConformanceReport.md#pointblank.ConformanceReport) (its `core` attribute).

`package: SubmissionPackage | None = None`  
The [`SubmissionPackage`](../reference/SubmissionPackage.md#pointblank.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`](../reference/ConformanceReport.md#pointblank.ConformanceReport.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:

``` python
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()`](../reference/ConformanceReport.md#pointblank.ConformanceReport.findings):

``` python
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.