yamaa / Benchmark

CDISC.ADSL

Text Mapping

input files
2
subjects
8
expected rows
4
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 reviewWhat this benchmark means
README.md

Goal: pin the text lookup contract: inline dictionaries, an external dictionary file, case-insensitive reads, and a regular-expression search with capture groups.

Input: DM carries one record per subject with coded sex, race, and treatment group values and a subject identifier that normally starts with a site prefix, such as UCSD-0123. The race dictionary is a YAML file beside the input.

Columns:

  • SEXC reads an inline dictionary with exact matching: M gives Male and F gives Female, while a code with no entry and a missing sex both give Unknown.
  • RACEC uses the same lookup contract with the dictionary loaded from the YAML file instead of written in the spec; a race with no entry or a missing race gives Unknown.
  • TRTGRPC reads case-insensitively: source and dictionary keys are folded to upper case before comparison, so lowercase and mixed-case treatment codes match their uppercase keys. A missing treatment group gives Unknown.
  • SITENUM matches each whole identifier against a regular expression (capital letters, a hyphen, digits) and keeps the second capture group, the digits after the prefix, as text, so leading zeros stay. An identifier of any other shape gives NONE, and a missing identifier gives MISSING.

Inputs

Under review2 source files

input/dm.csv

Edit4 rows
USUBJIDSEXRACETRTGRPSUBJID
MWHITEdrugUCSD-0123
FASIANDRUGUCSD-0456
UBLACK OR AFRICAN AMERICANPlacebobad-id
—WHITE——

input/race_dict.yaml

Edit
ASIAN: Asian
"BLACK OR AFRICAN AMERICAN": Black or African American
WHITE: White

Expected output

Under review4 expected rows

expected/adsl.csv

Edit4 rows
STUDYIDUSUBJIDSEXCRACECTRTGRPCSITENUM
YAMAA-01MaleWhiteDrug0123
YAMAA-01FemaleAsianDrug0456
YAMAA-01UnknownBlack or African AmericanPlaceboNONE
YAMAA-01UnknownWhiteUnknownMISSING
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
ADSLSTUDYIDStudy IdentifiercharacterSet constant value "YAMAA-01".Assigned
ADSLUSUBJIDUnique Subject IdentifiercharacterCopy value from DM.USUBJID.Predecessor
ADSLSEXCSex DecodedcharacterRecode DM.SEX: "M" → "Male", "F" → "Female"; missing or unlisted values → "Unknown".Derived
ADSLRACECRace DecodedcharacterRecode DM.RACE using the dictionary in input/race_dict.yaml; missing values → "Unknown"; unlisted values are errors.Derived
ADSLTRTGRPCPlanned Treatment DecodedcharacterRecode DM.TRTGRP: "DRUG" → "Drug", "PLACEBO" → "Placebo"; missing values → "Unknown"; unlisted values are errors; matching ignores letter case.Derived
ADSLSITENUMSite Number ExtractedcharacterExtract capture group 2 matching ^([A-Z]+)-(\d+)$ from DM.SUBJID; a missing source → "MISSING"; no match → "NONE".Derived
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

Edit4 lines
library(yamaa)adsl <- yamaa_domain("spec.yaml")$outputadsl

run.py

Edit4 lines
import yamaaadsl = yamaa.yamaa_domain("spec.yaml").outputadsl