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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: derive each subject's death study day and cause of death by joining their adverse events, and code the cause against a medical dictionary.

Input: one spec.yaml declaring three datasets. DM carries one row per subject. AE carries the adverse-event records: subject, event term, outcome, and study day. MEDDRA carries the dictionary: each lowest-level term beside its preferred term.

Lookups:

  • DEATHEV names each subject's fatal adverse event. Only fatal records are eligible, and the latest study day wins; the subject key is shared with the output, so it is inferred rather than restated. A subject with two fatal records takes the later one's day and term, and a subject with no fatal record keeps DTHDY and DTHCAUS blank.
  • The named intermediate codes the cause into DTHPTERM: the subject's cause is matched against the dictionary's lowest-level term and the preferred term comes back. A missing cause matches nothing, so the coded cause stays blank too, as it does for a cause the dictionary does not list.

Note: a join never changes the row count: every subject keeps exactly one row, and every read of the same lookup sees the one selected record. When no record is selected, each read answers with a blank rather than failing.

Inputs

Under review
3 source files

input/ae.csv

Edit
5 rows
input/ae.csv
USUBJIDAEDECODAEOUTAEDY
HEADACHENOT RECOVERED50
CARDIAC FAILUREFATAL100
MYOCARDIAL INFARCTIONFATAL150
STROKEFATAL80
NAUSEARECOVERED20

input/dm.csv

Edit
4 rows
input/dm.csv
USUBJID

input/meddict.csv

Edit
3 rows
input/meddict.csv
LLTNAMEPTNAME
MYOCARDIAL INFARCTIONAcute myocardial infarction
CARDIAC FAILURECardiac failure
STROKECerebrovascular accident

Expected output

Under review
4 expected rows

expected/adsl.csv

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4 rows
expected/adsl.csv
USUBJIDDTHDYDTHCAUSDTHPTERM
150MYOCARDIAL INFARCTIONAcute myocardial infarction
80STROKECerebrovascular accident
———
———
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
ADSLUSUBJIDUnique Subject IdentifiercharacterCopy value from DM.USUBJID.Predecessor
ADSLDTHDYStudy Day of DeathnumericTake AEDY from the last AE record matching on USUBJID, where AE.AEOUT = 'FATAL', ordered by AE.AEDY, and no match yields blank.Derived
ADSLDTHCAUSCause of DeathcharacterTake AEDECOD from the last AE record matching on USUBJID, where AE.AEOUT = 'FATAL', ordered by AE.AEDY, and no match yields blank.Derived
ADSLDTHPTERMCause of Death Preferred TermcharacterTake PTNAME from the MEDDRA record whose LLTNAME equals DTHCAUS, and no match yields blank.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

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