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Summary

Lifecycle: draftEditUnder review
What this benchmark means
README.md

Public entries: Python and R. Passing coverage requires an installed inventory.

Goal: show which branch of a first-match case a record takes when an input is missing: a comparison with a missing value is unknown rather than false, and only a true condition selects a branch.

Input: one spec.yaml over DM, carrying each subject's age, sex, and treatment group code. Age and group may be missing, and a code may be written in lower case.

Columns:

  • AGERISK is HIGH over 65, MEDIUM from 40 through 65, and LOW otherwise. The first true branch wins, so an age over 65 is HIGH although it also passes the test for 40. A missing age is unknown in both tests, never true, so it falls through to LOW.
  • SENIOR_M is Y for a male younger than 65 (its age test is negated), U when age is missing, and N otherwise. Negating an unknown comparison leaves it unknown, so a missing age never reaches Y and is caught by the explicit missing-age test instead.
  • TRTCLASS copies a screening code (one starting SCRN) through unchanged, and is DRUG_FAMILY for a code starting DRUG, STANDARD for PLACEBO, MISSING when the code is missing, and OTHER for any other code. Pattern matching is case-sensitive, so a lower-case drug-low is OTHER. A missing code is unknown, not true, in the test for "neither DRUG nor PLACEBO", so it reaches MISSING.

Note: a branch is taken only when its condition is true. A comparison with a missing value is unknown, and so is its negation, so a missing value never satisfies a "not" test either; only an explicit test for missingness catches it.

Inputs

Under review
1 source file

input/dm.csv

Edit
6 rows
input/dm.csv
USUBJIDAGESEXTRTGRP
70MDRUG
40FPLACEBO
—Mdrug-low
25F—
18MDRUGX
55FSCRN-01

Expected output

Under review
6 expected rows

expected/adsl.csv

Edit
6 rows
expected/adsl.csv
STUDYIDUSUBJIDAGERISKSENIOR_MTRTCLASS
YAMAA-01HIGHNDRUG_FAMILY
YAMAA-01MEDIUMNSTANDARD
YAMAA-01LOWUOTHER
YAMAA-01LOWNMISSING
YAMAA-01LOWYDRUG_FAMILY
YAMAA-01MEDIUMNSCRN-01
Shaded columns are derived— = missing value

Human review and assessment

Your review

2 questions and your comments · View benchmark 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
ADSLAGERISKAge Risk CategorycharacterIf DM.AGE > 65 then "HIGH"; If DM.AGE >= 40 then "MEDIUM"; otherwise "LOW".Derived
ADSLSENIOR_MMale Under 65 FlagcharacterIf NOT (DM.AGE >= 65) AND DM.SEX = 'M' then "Y"; If DM.AGE IS NULL then "U"; otherwise "N".Derived
ADSLTRTCLASSTreatment ClasscharacterIf DM.TRTGRP LIKE 'SCRN%' then "DM.TRTGRP"; If DM.TRTGRP LIKE 'DRUG%' then "DRUG_FAMILY"; If DM.TRTGRP NOT IN ('DRUG', 'PLACEBO') then "OTHER"; If DM.TRTGRP IS NULL then "MISSING"; otherwise "STANDARD".Derived
VersionDateAuthorDescriptionReviewerSign-off
1.72026-10-20yamaa docs buildRead 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
1.62026-10-17yamaa docs buildDescribe collected date parts written as ISO 8601 date text and times of day counted in seconds since midnight
1.52026-10-17yamaa docs buildName 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
1.42026-10-15yamaa docs buildState datetime precision codes through the hour and minute, and complete only the time parts a collected datetime lacks
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

Public calls generated from benchmark.yaml; results must match the expected output

R

Edit
3 lines
library(yamaa)yamaa_spec("spec.yaml")$run()$output

Python

Edit
3 lines
import yamaayamaa.spec("spec.yaml").run().output