What Was Checked

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: Would a real study need this derivation, specified this way? Does it follow CDISC ADaM: variable names, controlled terms, record structure? Flag any rule that is ambiguous or that a sponsor would handle differently.
Inputs: Do they look like data collected in a real trial, and do they include the situations you would expect to meet in one?
Expected output: Would independent QC programming reproduce every value from these inputs and rules?

The other tabs beside Comments are background for assessment, not under review.

Summary

EditUnder reviewWhat this benchmark means

README.md

Goal: record every check a run evaluates, held or violated: one row per check, in the order the run evaluated it.

Input: subject-level records with an AGE each; one age (214) falls outside the expected 18-to-100 range.

Checks:

not_missing on AGE: held, every age is present.
range on AGE: violated by the age outside 18-to-100; it is a warning, so the run still succeeds and the warning log carries the offending subject.
unique on USUBJID: held, no subject repeats.
row_count: held, the run produced at least one row.

The check log records REPORT_VERSION, ARTIFACT, SPEC_PATH, VERIFICATION_ID, CHECK, TARGET, REQUIREMENT, SEVERITY, OUTCOME, CONDITION, EVALUATED_COUNT, FAILURE_COUNT, and DETAILS. EVALUATED_COUNT counts what the check examines: rows for a row-by-row check, distinct subjects for unique, and the whole dataset as one unit for row_count. A held check leaves CONDITION empty, reports zero failures, and has empty details ({}).

The warning log records LOG_VERSION, ARTIFACT, SEVERITY, CONDITION, REQUIREMENT, SPEC_PATH, VERIFICATION_ID, FAILURE_COUNT, OFFENDING_KEYS, and DETAILS, so the offending subject can be investigated without stopping the run.

Note: the warning log says what broke and for whom; the check log says what was checked and what happened. The two join on SPEC_PATH, and only the warning log lists the offending subjects.

YAML specification

1 spec file

spec.yaml

Edit38 lines

1schema_version: "1.0"2domain: ADSL3keys: [USUBJID]4input:5  DM: input/dm.csv67output:8  path: adsl.csv9  columns: [USUBJID, AGE]10  # Violated warnings, with their offending keys, go to the warning log;11  # every declared check, held or violated, gets one line in the check log.12  warning_log: adsl-warnings.csv13  verification_log: adsl-checks.csv1415columns:16  - name: USUBJID17    type: str18    label: Unique Subject Identifier19    derivation: DM.USUBJID2021  - name: AGE22    type: int23    label: Age24    derivation: DM.AGE25    verifications:26      - not_missing: {}27      # A warning, not an error: an age outside 18-100 is logged and the28      # run still succeeds with every row kept.29      - range:30          min: 1831          max: 10032          severity: warning3334verifications:35  - unique:36      columns: [USUBJID]37  - row_count:38      min: 1

Inputs

Under review1 source file

input/dm.csv

Edit3 rows

input/dm.csv
STUDYID	USUBJID	AGE
STUDY01		30
STUDY01		214
STUDY01		45

Expected output

Under review8 expected rows

expected/adsl-checks.csv

Edit4 rows

expected/adsl-checks.csv
REPORT_VERSION	ARTIFACT	SPEC_PATH	VERIFICATION_ID	CHECK	TARGET	REQUIREMENT	SEVERITY	OUTCOME	CONDITION	EVALUATED_COUNT	FAILURE_COUNT	DETAILS
1.0	adsl.csv	columns.AGE.verifications[0].not_missing	—	not_missing	AGE	REQ-0375	error	held	—	3	0	{}
1.0	adsl.csv	columns.AGE.verifications[1].range	—	range	AGE	REQ-0377	warning	violated	range_failed	3	1	{"column":"AGE"}
1.0	adsl.csv	verifications[0].unique	—	unique	—	REQ-0381	error	held	—	3	0	{}
1.0	adsl.csv	verifications[1].row_count	—	row_count	—	REQ-0385	error	held	—	1	0	{}

expected/adsl-warnings.csv

Edit1 row

expected/adsl-warnings.csv
LOG_VERSION	ARTIFACT	SEVERITY	CONDITION	REQUIREMENT	SPEC_PATH	VERIFICATION_ID	FAILURE_COUNT	OFFENDING_KEYS	DETAILS
1.0	adsl.csv	warning	range_failed	REQ-0377	columns.AGE.verifications[1].range	—	1	[{"USUBJID":"S-2"}]	{"column":"AGE"}

expected/adsl.csv

Edit3 rows

expected/adsl.csv
USUBJID	AGE
	30
	214
	45

Shaded columns are derived— = missing value

Comments and assessment materials

Dataset Name	Variable Name	Variable Label	Type	Codelist/Controlled Terms	Core	Computational Method	Origin
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

run.R

Edit4 lines

1library(yamaa)23adsl <- yamaa_domain("spec.yaml")$output4adsl

run.py