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: flag adverse events whose dictionary-derived term contains one of the dermatologic criterion terms (APPLICATION, DERMATITIS, ERYTHEMA, or BLISTER), the kind of term search behind a customized query name such as CQ01NAM.

Input: one record per adverse event carrying AESEQ (sequence number) and AEDECOD (dictionary-derived term).

Variables:

  • CRIT1FL: Y when the term contains any of the four criterion terms anywhere in its text, as APPLICATION SITE DERMATITIS does; missing otherwise.
  • DERMFL: Y when the term contains DERM (as in DERMATITIS) or ERYTHEMA anywhere in its text; missing otherwise.

Note: each flag is Y or blank, never N. An event with no recorded term is blank as well: its search is unknown rather than a non-match, and only a match sets the flag.

Inputs

Under review
1 source file

input/ae.csv

Edit
7 rows
input/ae.csv
STUDYIDUSUBJIDAESEQAEDECOD
CATH-011HEADACHE
CATH-012APPLICATION SITE DERMATITIS
CATH-013NAUSEA
CATH-014ERYTHEMA
CATH-011BLISTER
CATH-012VOMITING
CATH-011—

Expected output

Under review
7 expected rows

expected/adae.csv

Edit
7 rows
expected/adae.csv
STUDYIDUSUBJIDAESEQAEDECODCRIT1FLDERMFL
CATH-011HEADACHE——
CATH-012APPLICATION SITE DERMATITISYY
CATH-013NAUSEA——
CATH-014ERYTHEMAYY
CATH-011BLISTERY—
CATH-012VOMITING——
CATH-011———
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 IdentifiercharacterCopy value from AE.STUDYID.Predecessor
ADAEUSUBJIDUnique Subject IdentifiercharacterCopy value from AE.USUBJID.Predecessor
ADAEAESEQSequence NumbernumericCopy value from AE.AESEQ.Predecessor
ADAEAEDECODDictionary-Derived TermcharacterCopy value from AE.AEDECOD.Predecessor
ADAECRIT1FLCriterion 1 Evaluation Result FlagcharacterY"Y" when str_contains(AE.AEDECOD, 'APPLICATION|DERMATITIS|ERYTHEMA|BLISTER'); blank otherwise.Derived
ADAEDERMFLDermatologic Event Flagcharacter"Y" when str_contains(AE.AEDECOD, 'DERM|ERYTHEMA'); blank otherwise.Derived
VariablePermitted values
CRIT1FLY
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