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Summary
EditUnder reviewWhat this benchmark meansREADME.mdPublic entries: Python and R. Passing coverage requires an installed inventory.
Goal: summarize each subject's dosing records three ways: a qualified reduction over the dosing table, a filtered and a per-record-derived variant of it, and an unqualified reduction over the built rows that is broadcast back to every row in its treatment group.
Input: DM carries one row per subject with a treatment group
(TRTGRP). EX carries the dosing records: subject, sequence, dose,
and treatment. Subject 04 has no dosing records; subject 03 has one
record whose dose is missing; subject 01 has three records, one with a
missing dose.
Aggregations:
NDOSEcounts dose records (COUNT(EX.*));NDOSEVALcounts only the records with a dose present (COUNT(EX.EXDOSE)).TOTDOSE,MEANDOSE, andMAXDOSEare the total, mean and largest of the present doses.DRUGDOSEsums only theDRUGrecords;PLACDOSEsums only thePLACEBOrecords, so subjects with no placebo record read as missing.TOTDOSE_MGbinds each record's dose in milligrams first (two per-record bindings, the second reading the first) and then sums the bound values.AVGNDOSEis unqualified: it averagesNDOSEover the built rows within each treatment group and broadcasts the result to every row of the group. Subject04's missingNDOSEis excluded from the average.
Note: a missing dose contributes nothing and never a zero: TOTDOSE
for subject 03 is missing while NDOSEVAL is 0. A subject with no
records at all (subject 04) reads every qualified reduction as missing,
including both counts. An empty group after filtering (PLACDOSE for
subject 01) also reads as missing.
Inputs
Under review2 source filesExpected output
Under review4 expected rowsexpected/adsl.csv
Edit4 rows| USUBJID | TRTGRP | NDOSE | NDOSEVAL | TOTDOSE | MEANDOSE | MAXDOSE | DRUGDOSE | PLACDOSE | TOTDOSE_MG | AVGNDOSE |
|---|---|---|---|---|---|---|---|---|---|---|
| A | 3 | 2 | 300 | 150 | 200 | 300 | — | 300000 | 2.5 | |
| A | 2 | 2 | 300 | 150 | 150 | 150 | 150 | 300000 | 2.5 | |
| B | 1 | 0 | — | — | — | — | — | — | 1 | |
| B | — | — | — | — | — | — | — | — | 1 |
Human review and assessment
| 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 | TRTGRP | Treatment Group | character | Copy value from DM.TRTGRP. | Predecessor | ||
| ADSL | NDOSE | Number of Dose Records | numeric | Aggregate COUNT(EX.*) over records matching on the shared output keys. | Derived | ||
| ADSL | NDOSEVAL | Number of Non-missing Doses | numeric | Aggregate COUNT(EX.EXDOSE) over records matching on the shared output keys. | Derived | ||
| ADSL | TOTDOSE | Total Dose | numeric | Aggregate SUM(EX.EXDOSE) over records matching on the shared output keys. | Derived | ||
| ADSL | MEANDOSE | Mean Dose | numeric | Aggregate MEAN(EX.EXDOSE) over records matching on the shared output keys. | Derived | ||
| ADSL | MAXDOSE | Maximum Dose | numeric | Aggregate MAX(EX.EXDOSE) over records matching on the shared output keys. | Derived | ||
| ADSL | DRUGDOSE | Total Drug Dose | numeric | Aggregate SUM(EX.EXDOSE) over records matching on the shared output keys, where EX.EXTRT = 'DRUG'. | Derived | ||
| ADSL | PLACDOSE | Total Placebo Dose | numeric | Aggregate SUM(EX.EXDOSE) over records matching on the shared output keys, where EX.EXTRT = 'PLACEBO'. | Derived | ||
| ADSL | TOTDOSE_MG | Total Dose in Milligrams | numeric | Aggregate SUM(DOSEMG) over records matching on the shared output keys, with DOSE as EX.EXDOSE, with DOSEMG as compute DOSE * 1000. | Derived | ||
| ADSL | AVGNDOSE | Mean Dose Records per Treatment Group | numeric | Aggregate MEAN(NDOSE) over records grouped by TRTGRP, matching on the shared output keys. | Derived |
| Version | Date | Author | Description | Reviewer | Sign-off |
|---|---|---|---|---|---|
| 1.7 | 2026-10-20 | yamaa docs build | Read a specification in the 1.0 surface as its 0.5 counterpart; state that missing values stay missing where a mapping declares no missing handler, and leave out a declared missing answer that restates a handler's default | ||
| 1.6 | 2026-10-17 | yamaa docs build | Describe collected date parts written as ISO 8601 date text and times of day counted in seconds since midnight | ||
| 1.5 | 2026-10-17 | yamaa docs build | Name collected precision in words instead of letter codes; state imputation fallbacks, minimum source precision, bounds ignored outside the collected period, and invalid text with no handler as an error; describe date and datetime shifts, imputation flag codes, and partial text giving no date part | ||
| 1.4 | 2026-10-15 | yamaa docs build | State datetime precision codes through the hour and minute, and complete only the time parts a collected datetime lacks | ||
| 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 |