yamaa 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 meansREADME.mdGoal: carry each subject's collected age (AGE) into the ADSL analysis
dataset, keeping values outside the expected range of 18 to 100 for later
data review.
Input: collected demographics, one record per subject, carrying age.
Variables:
AGE: the subject's age in years as collected. A value below 18 or above 100 stays in the dataset as collected, and a missing age stays missing; neither case stops the run.
Note: an age outside 18 to 100 does not stop the run: every subject keeps
its record, and the review log records one row for the failed check, naming
the dataset (ARTIFACT, LOG_VERSION), what was checked (CONDITION,
REQUIREMENT, SPEC_PATH, VERIFICATION_ID), how serious the failure is
(SEVERITY), and which subjects failed (FAILURE_COUNT, OFFENDING_KEYS,
DETAILS).
Inputs
Under review1 source fileinput/dm.csv
Edit8 rows| STUDYID | USUBJID | AGE |
|---|---|---|
| YAMAA-01 | 34 | |
| YAMAA-01 | 18 | |
| YAMAA-01 | 100 | |
| YAMAA-01 | 17 | |
| YAMAA-01 | 101 | |
| YAMAA-01 | 214 | |
| YAMAA-01 | — | |
| YAMAA-01 | 57 |
Expected output
Under review9 expected rowsexpected/adsl-violations.csv
Edit1 row| 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[0].range | — | 3 | [{"STUDYID":"YAMAA-01","USUBJID":"YAMAA-01-104"},{"STUDYID":"YAMAA-01","USUBJID":"YAMAA-01-105"},{"STUDYID":"YAMAA-01","USUBJID":"YAMAA-01-106"}] | {"column":"AGE"} |
expected/adsl.csv
Edit8 rows| STUDYID | USUBJID | AGE |
|---|---|---|
| YAMAA-01 | 34 | |
| YAMAA-01 | 18 | |
| YAMAA-01 | 100 | |
| YAMAA-01 | 17 | |
| YAMAA-01 | 101 | |
| YAMAA-01 | 214 | |
| YAMAA-01 | — | |
| YAMAA-01 | 57 |
Comments and assessment materials
| Dataset Name | Variable Name | Variable Label | Type | Codelist/Controlled Terms | Core | 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 |
| Version | Date | Author | Description | Reviewer | Sign-off |
|---|---|---|---|---|---|
| 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 |
prompts/adam-adsl-age-quality/full.md
Edit12 linesFollowing CDISC ADaM standards, use the provided DM dataset to create an ADSL dataset with one record per subject.
The output dataset should contain the following columns in this order: STUDYID, USUBJID, AGE
AGE is the subject's age in years as collected. A value below 18 or above 100 stays in the dataset as collected, and a missing age stays missing; neither case stops the run.
Read the source datasets from /app/input and save the completed dataset as /app/output/adsl.csv.
prompts/adam-adsl-age-quality/conventions.md
Edit8 linesFollowing CDISC ADaM standards, use the provided DM dataset to create an ADSL dataset with one record per subject.
The output dataset should contain the following columns in this order: STUDYID, USUBJID, AGE
Read the source datasets from /app/input and save the completed dataset as /app/output/adsl.csv.
prompts/adam-adsl-age-quality/brief.md
Edit8 linesFollowing CDISC ADaM standards, use the provided DM dataset to create an ADSL dataset with one record per subject.
The output dataset should contain the following columns in this order: STUDYID, USUBJID, AGE
Read the source datasets from /app/input and save the completed dataset as /app/output/adsl.csv.
run.R
Edit4 lineslibrary(yamaa)adsl <- yamaa_domain("spec.yaml")$outputadslrun.py
Edit4 linesimport yamaaadsl = yamaa.yamaa_domain("spec.yaml").outputadslsolutions/adam-adsl-age-quality/result.sas
Edit19 lines/* opensas reference solution for the yamaa benchmark adam-adsl-age-quality. Reads the benchmark inputs and derives the requested output. Execute with the existing opensas runtime: opensas /app/output/result.sas. */data dm; length STUDYID USUBJID $1024; infile "/app/input/dm.csv" dsd dlm="," firstobs=2 truncover lrecl=32767; input STUDYID : $char1024. USUBJID : $char1024. AGE;run;proc sql; create table result as select STUDYID, USUBJID, AGE from dm;quit;proc sql; create table final as select STUDYID, USUBJID, AGE from result;quit;proc export data=final outfile="/app/output/adsl.csv" dbms=csv replace; run;
Comments
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