Reject Missing Field

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 the age (AGE) for each subject the extract holds.

Input: EDC output in long form, one row per collected item; each subject has a sex row and an age row.

Variables:

AGE would be the age in whole years as collected. The collected rows it reads are also chosen by subject, under the name USUBJID, which those rows do not carry: they hold the subject as SubjectKey. The run is therefore rejected before any data is read, and no artifact is accepted.

How to fix

Decide what distinguishes the collected rows the age comes from, then say it in the words the records use. The collected rows carry the item they answer and the subject they belong to under the extract's own names:

- name: AGE
  type: int
  label: Age
  derivation:
    source:
      filter: ODM.ItemOID = 'IT.DM.AGE'
      variable: ODM.Value

The subject needs no mention: each record is built for one subject and reads only the rows that subject was collected under.

When the intent was to leave some subjects without an age, state that as a rule over the built record rather than over the collected rows, for example by mapping a collected exclusion flag.

Expected failure

Editthe run is rejected; no artifact is accepted

expected/error.yaml

phase: validation
condition: unknown_field
requirement: REQ-0132

expected/error.yaml

phase: validation
condition: unknown_field
spec_paths:
  - columns.AGE.derivation.source.filter
requirement: REQ-0132
context:
  identifier: USUBJID
  dataset: ODM

YAML specification

1 spec file

spec.yaml

Edit36 lines

1schema_version: "1.0"2domain: DM3keys: [STUDYID, USUBJID]45input:6  ODM: input/odm.csv78output:9  path: dm.csv10  columns: [DOMAIN, STUDYID, USUBJID, AGE]1112columns:13  - name: DOMAIN14    type: str15    label: Domain Abbreviation16    derivation: {literal: DM}1718  - name: STUDYID19    type: str20    label: Study Identifier21    derivation: ODM.StudyOID2223  - name: USUBJID24    type: str25    label: Unique Subject Identifier26    derivation: ODM.SubjectKey2728  # The filter can name only qualified input fields. USUBJID is an output29  # column; the collected rows carry the subject as ODM.SubjectKey.30  - name: AGE31    type: int32    label: Age33    derivation:34      source:35        filter: ODM.ItemOID = 'IT.DM.AGE' AND USUBJID <> '002'36        variable: ODM.Value

Inputs

Under review1 source file

input/odm.csv

Edit4 rows

input/odm.csv
StudyOID	MetaDataVersionOID	SubjectKey	StudyEventOID	StudyEventRepeatKey	ItemGroupOID	ItemGroupRepeatKey	ItemOID	Value
STUDY01	MDV.1	001	SCREENING	1	IG.DM	1	IT.DM.SEX	Male
STUDY01	MDV.1	001	SCREENING	1	IG.DM	1	IT.DM.AGE	34
STUDY01	MDV.1	002	SCREENING	1	IG.DM	1	IT.DM.SEX	Female
STUDY01	MDV.1	002	SCREENING	1	IG.DM	1	IT.DM.AGE	28

Unexpected Output

Under reviewNo artifact is produced

No fixture files in this directory.

Shaded columns are derived— = missing value

Comments and assessment materials

Variable Name	Variable Label	Type	Origin	Conversion Definition	Variable Type	Variable Order
DOMAIN	Domain Abbreviation	Char	Assigned	Set constant value "DM".	SDTM	1
STUDYID	Study Identifier	Char	Collected	Copy value from ODM.StudyOID.	SDTM	2
USUBJID	Unique Subject Identifier	Char	Collected	Copy value from ODM.SubjectKey.	SDTM	3
AGE	Age	Num	Collected	Copy value from ODM.Value where ODM.ItemOID = 'IT.DM.AGE' AND USUBJID <> '002'.	SDTM	4

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/odm.csv

Comments

Mapping spec