Reject Flag Without Missing Value

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: build ELDFL to mark each subject aged 65 or older with Y and every younger subject with N.

Input: collected demographics carrying numeric age (AGE), including one subject with no recorded age.

Variables:

AGE is the collected age, missing where it was not collected.
ELDFL would be Y for subjects aged 65 or older and N for younger subjects.

Note: the flag names N for a false condition but not what a subject with no recorded age receives, so the run is rejected before any data is read and no artifact is accepted.

How to fix

State what an unknown age returns. To give N to every subject not known to be 65 or older:

flag:
  condition: AGE >= 65
  false_value: N
  missing: N

Write missing: null instead to leave the flag missing when the age is unknown.

Expected failure

Editthe run is rejected; no artifact is accepted

expected/error.yaml

phase: validation
condition: missing_value_required
requirement: REQ-1258

expected/error.yaml

phase: validation
condition: missing_value_required
spec_paths:
  - columns.ELDFL.derivation.flag.missing
requirement: REQ-1258
context:
  false_value: "N"

YAML specification

1 spec file

spec.yaml

Edit35 lines

1schema_version: "1.0"2domain: ADSL3keys: [STUDYID, USUBJID]4input:5  DM: input/dm.csv67output:8  path: adsl.csv9  columns: [STUDYID, USUBJID, AGE, ELDFL]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.AGE2627  # The flag names a value for a false condition but none for an unknown28  # one.29  - name: ELDFL30    type: str31    label: Elderly Flag32    derivation:33      flag:34        condition: "AGE >= 65"35        false_value: "N"

Inputs

Under review1 source file

input/dm.csv

Edit3 rows

input/dm.csv
STUDYID	AGE	SEX
YAMAA-01	72	M
YAMAA-01	54	F
YAMAA-01	—	F

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
ADSL	ELDFL	Elderly Flag	character	"Y" when AGE >= 65; "N" otherwise; blank when the condition is unknown.	Derived

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