Reject Implausible 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: carry each subject's age in AGE from the collected demographics, accepting only ages from 18 to 100.

Input: collected demographics carrying the subject's age in AGE, identified by study and subject.

Variables:

AGE would contain the subject's age, taken from the collected age; missing when no age was collected.

Note: an age below 18 or above 100 is rejected once the dataset is complete, so no artifact is accepted; a missing age is not rejected.

How to fix

Query and correct the source age for YAMAA-01-102 if 214 is a data-entry error, then rerun the unchanged range check. If the protocol genuinely permits the confirmed value, revise the check boundary to the protocol's documented limit. Do not remove or widen the check merely to make an unconfirmed value pass.

Expected failure

Editthe run is rejected; no artifact is accepted

expected/error.yaml

phase: verification
condition: range_failed
requirement: REQ-0377

expected/error.yaml

phase: verification
condition: range_failed
spec_paths:
  - columns.AGE.verifications[0].range
requirement: REQ-0377
context:
  column: AGE
  failure_count: 1
  keys:
    - STUDYID: YAMAA-01
      USUBJID: YAMAA-01-102

YAML specification

1 spec file

spec.yaml

Edit31 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    # Ages 18 to 100 inclusive pass, and so does a missing age; any other age27    # fails the run once the dataset is complete.28    verifications:29      - range:30          min: 1831          max: 100

Inputs

Under review1 source file

input/dm.csv

Edit5 rows

input/dm.csv
STUDYID	USUBJID	AGE
YAMAA-01		64
YAMAA-01		214
YAMAA-01		18
YAMAA-01		100
YAMAA-01		—

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