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: 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.
  2. Inputs: Do they look like data collected in a real trial, and do they include the situations you would expect to meet in one?
  3. 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

Lifecycle: draftEditUnder review
What this benchmark means
README.md

Goal: demonstrate the per-record derive step of an aggregation: convert text questionnaire responses to numbers, then sum them.

Input: questionnaire responses with text results (QSORRES) for the physical functioning scale. Each subject and visit with a record for the first item (PF01) gets one score record.

Variables:

  • PARAMCD: PFSCORE on the score record.
  • PARAM: the subscale name.
  • AVAL: the sum of the visit's physical functioning responses, each read as a number. A blank response is left out of the sum; a response that is not a number stops the run.

Note: there is no separate text-to-number step. Declaring each record's response as a decimal number is what converts it, one record at a time, before the sum runs.

Inputs

Under review
1 source file

input/qs.csv

Edit
4 rows
input/qs.csv
STUDYIDUSUBJIDVISITQSSEQQSCATQSTESTCDQSTESTQSORRES
PILOT7BASELINE1PHYSICAL FUNCTIONING SCALEPF01Trouble with strenuous activity2
PILOT7BASELINE2PHYSICAL FUNCTIONING SCALEPF02Trouble with a long walk1
PILOT7BASELINE3PHYSICAL FUNCTIONING SCALEPF03Trouble with a short walk2
PILOT7BASELINE4PHYSICAL FUNCTIONING SCALEPF04Needs to stay in bed or a chair1

Expected output

Under review
1 expected row

expected/adqs.csv

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1 row
expected/adqs.csv
STUDYIDUSUBJIDVISITPARAMCDPARAMAVAL
PILOT7BASELINEPFSCOREPhysical Functioning Subscale Score6
Shaded columns are derived— = missing value

Comments and assessment materials

Comments

Signed in with GitHub; saved in repository Discussions

Mapping spec

Generated from the benchmark YAML; do not edit by hand

Dataset NameVariable NameVariable LabelTypeCodelist/Controlled TermsCoreComputational MethodOrigin
ADQSSTUDYIDStudy IdentifiercharacterCopy value from QS.STUDYID.Predecessor
ADQSUSUBJIDUnique Subject IdentifiercharacterCopy value from QS.USUBJID.Predecessor
ADQSVISITVisit NamecharacterCopy value from QS.VISIT.Predecessor
ADQSPARAMCDParameter CodecharacterSet per constructed row; see the Row construction sheet.Derived
ADQSPARAMParametercharacterSet per constructed row; see the Row construction sheet.Derived
ADQSAVALAnalysis ValuenumericAggregate SUM(QSNUM) over records matching on STUDYID, USUBJID and VISIT, where QS.QSCAT = 'PHYSICAL FUNCTIONING SCALE', with QSNUM as QS.QSORRES.Derived
TemplateInclude whenPARAMCDPARAM
subscaleQS.QSCAT = 'PHYSICAL FUNCTIONING SCALE' AND QS.QSTESTCD = 'PF01'Constant "PFSCORE"Constant "Physical Functioning Subscale Score"
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

yamaa code

Runs the yamaa specification; its result must match the expected output

run.R

Edit
4 lines
library(yamaa)adqs <- yamaa_domain("spec.yaml")$outputadqs

run.py

Edit
4 lines
import yamaaadqs = yamaa.yamaa_domain("spec.yaml").outputadqs