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.

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

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Summary

Lifecycle: reviewedEditUnder review
What this benchmark means
README.md

Goal: carry the subject randomization date RANDDT from demographics into the subject-level analysis file.

Input: a subject-level demographics file holding the study and subject identifiers with RANDDT, produced from an Operational Data Model (ODM) extract.

Variables:

  • RANDDT would be the subject's randomization date, taken from RANDDT in the demographics file.

Note: the date's value kind is already fixed where the demographics file is produced. Stating it again beside the source would leave two authorities for the same field even when they agree, so the run is rejected before any data is read and no artifact is accepted.

How to fix

Keep the producing DM specification as the single type authority and remove the inline types entry:

input:
  DM:
    path: input/dm.csv
    schema: input/dm.schema.yaml

Expected failure

Edit
the run is rejected; no artifact is accepted
expected/error.yaml
phase
validation
condition
redundant_field_type
requirement
REQ-0523
expected/error.yaml
phase: validation
condition: redundant_field_type
spec_paths:
  - input.DM.types.RANDDT
requirement: REQ-0523
context:
  dataset: DM
  field: RANDDT
  type: date

Inputs

Under review
3 source files

input/dm.csv

Edit
1 row
input/dm.csv
STUDYIDUSUBJIDRANDDT
YAMAA-012025-01-12

input/dm.schema.yaml

Edit
schema_version: "1.0"
domain: DM
keys: [STUDYID, USUBJID]
input:
  ODM: odm.csv

output:
  path: dm.csv
  columns: [STUDYID, USUBJID, RANDDT]

columns:
  - name: STUDYID
    type: str
    label: Study Identifier
    derivation: ODM.StudyOID

  - name: USUBJID
    type: str
    label: Unique Subject Identifier
    derivation: ODM.SubjectKey

  - name: RANDDT
    type: date
    label: Date of Randomization
    derivation: ODM.Value

rows:
  - id: subject
    filter: "ODM.ItemOID = 'IT.DM.RANDDT'"
    derivations: {}

input/odm.csv

Edit
1 row
input/odm.csv
StudyOIDMetaDataVersionOIDSubjectKeyStudyEventOIDStudyEventRepeatKeyItemGroupOIDItemGroupRepeatKeyItemOIDValue
YAMAA-01MDV.1YAMAA-01-001SCREENING1IG.DM1IT.DM.RANDDT2025-01-12

Unexpected Output

Under review
No artifact is produced

No fixture files in this directory.

Shaded columns are derived— = missing value

Comments and assessment materials

Comments

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Mapping spec

Generated from the benchmark YAML; do not edit by hand

Dataset NameVariable NameVariable LabelTypeCodelist/Controlled TermsCoreComputational MethodOrigin
ADSLSTUDYIDStudy IdentifiercharacterCopy value from DM.STUDYID.Predecessor
ADSLUSUBJIDUnique Subject IdentifiercharacterCopy value from DM.USUBJID.Predecessor
ADSLRANDDTDate of RandomizationnumericISO 8601Copy value from DM.RANDDT.Predecessor
VersionDateAuthorDescriptionReviewerSign-off
1.32026-09-29yamaa docs buildShow study codelist names, external dictionaries, and ISO 8601 formats in variable rows; list the document's codelist values and metadata
1.22026-09-29yamaa docs buildClassify ADaM copies from a declared input as Predecessor and recodes as Derived, and SDTM recodes of locally derived columns as Derived
1.12026-09-29yamaa docs buildResolve lookups into the donor record they select, and give every derivation shape a plain-language rule instead of its raw YAML
1.0yamaa docs buildInitial generation from spec.yaml