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 standards: 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: fix the order of the output rows: two sort terms, a placement for missing values set apart from the sort direction, and a stable order for ties.

Input: one spec.yaml over one adverse-event dataset AE, stored out of order, carrying subject, sequence number, decoded term (AEDECOD, copied through unchanged), and analysis start day (AESTDY), which may be missing.

Order:

  • first by analysis start day (AESTDY), ascending, with missing days placed before every present day;
  • then, among rows on the same day or both missing a day, by sequence number (AESEQ), descending.

Note: missing values go where the order places them, first or last, whatever its direction, so an ascending sort can still put missing days first. Rows equal on both terms keep their input order.

Inputs

Under review
1 source file

input/ae.csv

Edit
8 rows
input/ae.csv
USUBJIDAESEQAEDECODAESTDY
3Nausea5
1Rash7
1Headache—
1Hives7
2Nausea5
1Fatigue3
2Dizziness—
4Vomiting1

Expected output

Under review
8 expected rows

expected/adae.csv

Edit
8 rows
expected/adae.csv
STUDYIDUSUBJIDAESEQAEDECODAESTDY
YAMAA-012Dizziness—
YAMAA-011Headache—
YAMAA-014Vomiting1
YAMAA-011Fatigue3
YAMAA-013Nausea5
YAMAA-012Nausea5
YAMAA-011Rash7
YAMAA-011Hives7
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
ADAESTUDYIDStudy IdentifiercharacterSet constant value "YAMAA-01".Assigned
ADAEUSUBJIDUnique Subject IdentifiercharacterCopy value from AE.USUBJID.Predecessor
ADAEAESEQAdverse Event Sequence NumbernumericCopy value from AE.AESEQ.Predecessor
ADAEAEDECODDictionary-Derived TermcharacterCopy value from AE.AEDECOD.Predecessor
ADAEAESTDYAnalysis Start Relative DaynumericCopy value from AE.AESTDY.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

yamaa code

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

run.R

Edit
4 lines
library(yamaa)adae <- yamaa_domain("spec.yaml")$outputadae

run.py

Edit
4 lines
import yamaaadae = yamaa.yamaa_domain("spec.yaml").outputadae