Reject Missing Age

Review ask

As a clinical trial statistician or programmer, challenge anything in this benchmark: describe a problem in Comments, or propose a fix with the Edit button on any source file.

Task: Is this a specification error a study team could realistically make, and should the build stop on it rather than continue?
Inputs: Do they trigger the failure for the reason the summary gives?
Expected failure: Is this the right error, raised at the right point, and would it tell the programmer what to fix?

The other tabs beside Comments are background for assessment, not under review.

Summary

EditUnder reviewWhat this benchmark means

README.md

Goal: record AGE for every subject from the collected demographics, requiring a value for each subject.

Input: collected demographics carrying age (AGE).

Variables:

AGE would be the subject's age, copied from the collected records.

Note: a subject with no recorded age leaves it missing, so the check on the completed dataset fails and no artifact is accepted.

How to fix

Decide whether the study can proceed without the value, then either correct the data or loosen the rule. When the age exists on the case report form, correct it at the governed source and rerun. When absence is genuinely possible, drop the not_missing check and let that subject's age stay missing:

- name: AGE
  type: int
  label: Age
  derivation: DM.AGE

Do not fill the gap with a placeholder age, which reports a value nobody recorded.

Expected failure

Editthe run is rejected; no artifact is accepted

expected/error.yaml

phase: verification
condition: not_missing_failed
requirement: REQ-0375

expected/error.yaml

phase: verification
condition: not_missing_failed
spec_paths:
  - columns.AGE.verifications[0].not_missing
requirement: REQ-0375
context:
  column: AGE
  failure_count: 1
  keys:
    - STUDYID: PILOT7
      USUBJID: P7-812

YAML specification

1 spec file

spec.yaml

Edit27 lines

1schema_version: "1.0"2domain: ADSL3keys: [STUDYID, USUBJID]4input:5  DM: input/dm.csv67output:8  path: adsl.csv9  columns: [STUDYID, USUBJID, AGE]1011columns:12  - name: STUDYID13    type: str14    label: Study Identifier15    derivation: DM.STUDYID1617  - name: USUBJID18    type: str19    label: Unique Subject Identifier20    derivation: DM.USUBJID2122  - name: AGE23    type: int24    label: Age25    derivation: DM.AGE26    verifications:27      - not_missing: {}

Inputs

Under review1 source file

input/dm.csv

Edit2 rows

input/dm.csv
STUDYID	USUBJID	AGE
PILOT7		54
PILOT7		—

Unexpected Output

Under reviewNo artifact is produced

No fixture files in this directory.

Shaded columns are derived— = missing value

Comments and assessment materials

Dataset Name	Variable Name	Variable Label	Type	Computational Method	Origin
ADSL	STUDYID	Study Identifier	character	Copy value from DM.STUDYID.	Predecessor
ADSL	USUBJID	Unique Subject Identifier	character	Copy value from DM.USUBJID.	Predecessor
ADSL	AGE	Age	numeric	Copy value from DM.AGE.	Predecessor

Version	Date	Author	Description
1.3	2026-09-29	yamaa docs build	Show study codelist names, external dictionaries, and ISO 8601 formats in variable rows; list the document's codelist values and metadata
1.2	2026-09-29	yamaa docs build	Classify ADaM copies from a declared input as Predecessor and recodes as Derived, and SDTM recodes of locally derived columns as Derived
1.1	2026-09-29	yamaa docs build	Resolve lookups into the donor record they select, and give every derivation shape a plain-language rule instead of its raw YAML
1.0		yamaa docs build	Initial generation from spec.yaml

How to fix

input/dm.csv

Comments

Mapping spec