yamaa 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.
- 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.
- Inputs: Do they look like data collected in a real trial, and do they include the situations you would expect to meet in one?
- 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
EditUnder reviewWhat this benchmark meansREADME.mdGoal: one Analysis Dataset for Adverse Events (ADAE) row per
collected adverse event (AE), carrying AEDECOD and AESEV.
Input: collected adverse events with their coded terms, plus supplemental records carrying the severity recorded for each event.
Variables:
AEDECODis the dictionary-derived term collected for the event; always present from the collected record.AESEVis the severity/intensity recorded for that event; empty when the event has no supplemental record, or the record carries no severity.
Note: severity is matched on the study and subject identifiers together with the event sequence number, so two events for one subject keep their own severities apart. A supplemental record for an event that was never collected is ignored: it creates no row.
Inputs
Under review2 source filesExpected output
Under review8 expected rowsexpected/adae.csv
Edit8 rows| STUDYID | USUBJID | AESEQ | AEDECOD | AESEV |
|---|---|---|---|---|
| KN189 | 1 | HEART ATTACK | SEVERE | |
| KN189 | 2 | NAUSEA | — | |
| KN189 | 3 | HEADACHE | MODERATE | |
| KN189 | 1 | HEADACHE | MILD | |
| KN189 | 2 | VOMITING | — | |
| KN189 | 1 | FATIGUE | — | |
| KN189 | 1 | RASH | — | |
| KN189 | 2 | NAUSEA | SEVERE |
Comments and assessment materials
| Dataset Name | Variable Name | Variable Label | Type | Codelist/Controlled Terms | Core | Computational Method | Origin |
|---|---|---|---|---|---|---|---|
| ADAE | STUDYID | Study Identifier | character | Copy value from AE.STUDYID. | Predecessor | ||
| ADAE | USUBJID | Unique Subject Identifier | character | Copy value from AE.USUBJID. | Predecessor | ||
| ADAE | AESEQ | Sequence Number | numeric | Copy value from AE.AESEQ. | Predecessor | ||
| ADAE | AEDECOD | Dictionary-Derived Term | character | Copy value from AE.AEDECOD. | Predecessor | ||
| ADAE | AESEV | Severity/Intensity | character | Copy value from SUPP.AESEV. | Predecessor |
| Version | Date | Author | Description | Reviewer | Sign-off |
|---|---|---|---|---|---|
| 1.3 | 2026-09-29 | yamaa docs build | Show study codelist names, external dictionaries, and ISO 8601 formats in variable rows; list the document's codelist values and metadata | ||
| 1.2 | 2026-09-29 | yamaa docs build | Classify ADaM copies from a declared input as Predecessor and recodes as Derived, and SDTM recodes of locally derived columns as Derived | ||
| 1.1 | 2026-09-29 | yamaa docs build | Resolve lookups into the donor record they select, and give every derivation shape a plain-language rule instead of its raw YAML | ||
| 1.0 | yamaa docs build | Initial generation from spec.yaml |
prompts/adam-adae-severity/full.md
Edit16 linesFollowing CDISC ADaM standards, use the provided AE and SUPP datasets to create an ADAE dataset with one record per adverse event.
The output dataset should contain the following columns in this order: STUDYID, USUBJID, AESEQ, AEDECOD, AESEV
AEDECOD is the dictionary-derived term collected for the event. AESEV is the severity recorded for that event, matched on the study and subject identifiers together with the event sequence number, so two events for one subject keep their own severities apart. AESEV has no value when the event has no supplemental record, or the record carries no severity. A supplemental record for an event that was never collected creates no row.
Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.
prompts/adam-adae-severity/conventions.md
Edit8 linesFollowing CDISC ADaM standards, use the provided AE and SUPP datasets to create an ADAE dataset with one record per adverse event.
The output dataset should contain the following columns in this order: STUDYID, USUBJID, AESEQ, AEDECOD, AESEV
Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.
prompts/adam-adae-severity/brief.md
Edit8 linesFollowing CDISC ADaM standards, use the provided AE and SUPP datasets to create an ADAE dataset with one record per adverse event.
The output dataset should contain the following columns in this order: STUDYID, USUBJID, AESEQ, AEDECOD, AESEV
Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.
run.R
Edit4 lineslibrary(yamaa)adae <- yamaa_domain("spec.yaml")$outputadaerun.py
Edit4 linesimport yamaaadae = yamaa.yamaa_domain("spec.yaml").outputadaesolutions/adam-adae-severity/result.R
Edit29 lines# Reference solution for the yamaa benchmark adam-adae-severity (R track).## Written from the benchmark's full prompt and its inputs alone, as an agent# would write it. Harbor's oracle agent runs it to check that the prompt can# be solved and that the grader scores a correct answer 1.library(dplyr, warn.conflicts = FALSE)library(readr)ae <- read_csv( "/app/input/ae.csv", col_types = cols(AESEQ = col_integer(), .default = col_character()))supp <- read_csv( "/app/input/supp.csv", col_types = cols(AESEQ = col_integer(), .default = col_character())) |> select(STUDYID, USUBJID, AESEQ, AESEV)# Severity is matched on the study and subject identifiers together with# the event sequence number. An event with no supplemental record, or a# record carrying no severity, has no value.adae <- ae |> left_join(supp, by = c("STUDYID", "USUBJID", "AESEQ")) |> mutate(AESEV = if_else(is.na(AESEV) | AESEV == "", NA_character_, AESEV)) |> select(STUDYID, USUBJID, AESEQ, AEDECOD, AESEV)dir.create("/app/output", showWarnings = FALSE)write_csv(adae, "/app/output/adae.csv", na = "")solutions/adam-adae-severity/result.py
Edit26 lines# Reference solution for the yamaa benchmark adam-adae-severity (Python track).## Written from the benchmark's full prompt and its inputs alone, as an agent# would write it. Harbor's oracle agent runs it to check that the prompt can# be solved and that the grader scores a correct answer 1.from pathlib import Pathimport polars as plae = pl.read_csv("/app/input/ae.csv", infer_schema=False).with_columns( pl.col("AESEQ").cast(pl.Int64))supp = pl.read_csv("/app/input/supp.csv", infer_schema=False).with_columns( pl.col("AESEQ").cast(pl.Int64)).select("STUDYID", "USUBJID", "AESEQ", "AESEV")# Severity is matched on the study and subject identifiers together with# the event sequence number. An event with no supplemental record, or a# record carrying no severity, has no value.adae = ae.join( supp, on=["STUDYID", "USUBJID", "AESEQ"], how="left", maintain_order="left").select("STUDYID", "USUBJID", "AESEQ", "AEDECOD", "AESEV")Path("/app/output").mkdir(exist_ok=True)adae.write_csv("/app/output/adae.csv")solutions/adam-adae-severity/result.sas
Edit25 lines/* opensas reference solution for the yamaa benchmark adam-adae-severity. Reads the benchmark inputs and derives the requested output. Execute with the existing opensas runtime: opensas /app/output/result.sas. */data ae; length STUDYID USUBJID AEDECOD $1024; infile "/app/input/ae.csv" dsd dlm="," firstobs=2 truncover lrecl=32767; input STUDYID : $char1024. USUBJID : $char1024. AESEQ AEDECOD : $char1024.;run;data supp; length STUDYID USUBJID AESEV $1024; infile "/app/input/supp.csv" dsd dlm="," firstobs=2 truncover lrecl=32767; input STUDYID : $char1024. USUBJID : $char1024. AESEQ AESEV : $char1024.;run;proc sql; create table result as select a.*,b.AESEV from ae as a left join supp as b on a.STUDYID=b.STUDYID and a.USUBJID=b.USUBJID and a.AESEQ=b.AESEQ;quit;proc sql; create table final as select STUDYID, USUBJID, AESEQ, AEDECOD, AESEV from result;quit;proc export data=final outfile="/app/output/adae.csv" dbms=csv replace; run;
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