Reject Coded 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: build AGEGRP to place each subject into an age band, with younger below 65 and elderly at or above 65.

Input: collected demographics carrying numeric age (AGE) and coded sex (SEX).

Variables:

AGEGRP would be younger below 65 and elderly at or above 65.

Note: the bands are applied to the coded sex (SEX) instead of the age. Placing a coded value into numeric bands has no meaning, so the run is rejected before any data is read and no artifact is accepted.

How to fix

Band the numeric age rather than the coded sex. The demographics file is read as text unless a field is given a type, so declare AGE as a whole number on the input:

input:
  DM: {path: input/dm.csv, types: {AGE: int}}

and band that age:

cut:
  source: DM.AGE
  breaks: [65]
  labels: [younger, elderly]

Expected failure

Editthe run is rejected; no artifact is accepted

expected/error.yaml

phase: validation
condition: incompatible_input_type
requirement: REQ-0306

expected/error.yaml

phase: validation
condition: incompatible_input_type
spec_paths:
  - columns.AGEGRP.derivation.cut.source
requirement: REQ-0306
context:
  source: DM.SEX
  expected: numeric
  actual: str

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, AGEGRP]1011columns:12  - name: STUDYID13    type: str14    label: Study Identifier15    derivation: DM.STUDYID1617  - name: USUBJID18    type: str19    label: Unique Subject Identifier20    derivation: DM.USUBJID2122  # The age bands are applied to the collected coded sex, a text field,23  # where a number such as the age belongs.24  - name: AGEGRP25    type: str26    label: Age Group27    derivation:28      cut:29        source: DM.SEX30        breaks: [65]31        labels: [younger, elderly]

Inputs

Under review1 source file

input/dm.csv

Edit2 rows

input/dm.csv
STUDYID	USUBJID	AGE	SEX
YAMAA-01		72	M
YAMAA-01		54	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	AGEGRP	Age Group	character	Band DM.SEX at 65 into "younger" and "elderly", intervals closed on the left.	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