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: reviewedEditUnder review
What this benchmark means
README.md

Goal: number each subject's serious events (AESER is Y) in onset order as SERSEQ, so the earliest serious event carries 1.

Input: adverse event records carrying the collection sequence (AESEQ), the serious flag (AESER), and the onset date (AESTDTC, carried as ASTDT; empty when the onset was never collected).

Variables:

  • SERSEQ holds the running number of the subject's serious events, ordered by onset date. Events sharing one onset date follow the order of their collection sequence, and a serious event with no onset date is numbered last. It stays empty for events that are not serious.

Note: numbering restarts for each subject, so a subject with no serious event keeps every record unnumbered.

Inputs

Under review
1 source file

input/ae.csv

Edit
10 rows
input/ae.csv
STUDYIDUSUBJIDAESEQAETERMAESERAESTDTC
PILOT71NauseaN2023-02-11
PILOT72PneumonitisY2023-03-04
PILOT73NeutropeniaY2023-02-20
PILOT74PneumoniaY2023-02-20
PILOT75AnemiaY—
PILOT71FatigueN2023-01-15
PILOT72HeadacheN2023-02-02
PILOT73DizzinessN—
PILOT71RashY2023-05-10
PILOT72VomitingN2023-05-12

Expected output

Under review
10 expected rows

expected/adae.csv

Edit
10 rows
expected/adae.csv
STUDYIDUSUBJIDAESEQAESERASTDTSERSEQ
PILOT71N2023-02-11—
PILOT72Y2023-03-043
PILOT73Y2023-02-201
PILOT74Y2023-02-202
PILOT75Y—4
PILOT71N2023-01-15—
PILOT72N2023-02-02—
PILOT73N——
PILOT71Y2023-05-101
PILOT72N2023-05-12—
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
ADAEAESERSerious EventcharacterY, NCopy value from AE.AESER.Predecessor
ADAEASTDTAnalysis Start DatenumericISO 8601Copy value from AE.AESTDTC.Predecessor
ADAESERSEQSerious Event Sequence NumbernumericRow number within each group of STUDYID, USUBJID, ordered by ASTDT then AESEQ.Derived
VariablePermitted values
AESERY, N
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

Agent prompt

The request an agent receives with the input files; each tier states no more than the one above

prompts/adam-adae-serious-sequence/full.md

Edit
15 lines

Following CDISC ADaM standards, use the provided AE dataset 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, AESER, ASTDT, SERSEQ

ASTDT is the onset date, and has no value when the onset was never collected. SERSEQ numbers the subject's serious events in onset order, so the earliest serious event carries 1. Events sharing one onset date follow collection sequence order, and a serious event with no onset date is numbered last. SERSEQ has no value for events that are not serious. Numbering restarts for each subject.

Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.

prompts/adam-adae-serious-sequence/conventions.md

Edit
8 lines

Following CDISC ADaM standards, use the provided AE dataset 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, AESER, ASTDT, SERSEQ

Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.

prompts/adam-adae-serious-sequence/brief.md

Edit
8 lines

Following CDISC ADaM standards, use the provided AE dataset 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, AESER, ASTDT, SERSEQ

Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.

yamaa code

Runs the yamaa specification; its result must match the expected output

run.R

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4 lines
library(yamaa)adae <- yamaa_domain("spec.yaml")$outputadae

run.py

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4 lines
import yamaaadae = yamaa.yamaa_domain("spec.yaml").outputadae

Solution without yamaa

Written from the full prompt and the inputs alone; CI checks it reproduces the expected output

solutions/adam-adae-serious-sequence/result.R

Edit
33 lines
# Reference solution for the yamaa benchmark adam-adae-serious-sequence (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()))# Number the subject's serious events in onset order, so the earliest# carries 1. Events sharing one onset date follow collection order, and# a serious event with no onset date is numbered last.numbered <- ae |>  filter(AESER %in% "Y") |>  mutate(ASTDT = if_else(is.na(AESTDTC) | AESTDTC == "", NA_character_, AESTDTC)) |>  arrange(USUBJID, is.na(ASTDT), ASTDT, AESEQ) |>  group_by(USUBJID) |>  mutate(SERSEQ = row_number()) |>  ungroup() |>  select(USUBJID, AESEQ, SERSEQ)adae <- ae |>  mutate(ASTDT = if_else(is.na(AESTDTC) | AESTDTC == "", NA_character_, AESTDTC)) |>  left_join(numbered, by = c("USUBJID", "AESEQ")) |>  select(STUDYID, USUBJID, AESEQ, AESER, ASTDT, SERSEQ)dir.create("/app/output", showWarnings = FALSE)write_csv(adae, "/app/output/adae.csv", na = "")

solutions/adam-adae-serious-sequence/result.py

Edit
34 lines
# Reference solution for the yamaa benchmark adam-adae-serious-sequence (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))# Number the subject's serious events in onset order, so the earliest# carries 1. Events sharing one onset date follow collection order, and# a serious event with no onset date is numbered last. ISO dates sort# as text. Numbering restarts for each subject.numbered = (    ae.filter(pl.col("AESER") == "Y")    .with_columns(ASTDT=pl.col("AESTDTC"))    .sort(["USUBJID", "AESTDTC", "AESEQ"], nulls_last=True)    .with_columns(SERSEQ=pl.col("AESEQ").cum_count().over("USUBJID"))    .select("USUBJID", "AESEQ", "SERSEQ"))adae = (    ae.with_columns(ASTDT=pl.col("AESTDTC"))    .join(numbered, on=["USUBJID", "AESEQ"], how="left", maintain_order="left")    .select("STUDYID", "USUBJID", "AESEQ", "AESER", "ASTDT", "SERSEQ"))Path("/app/output").mkdir(exist_ok=True)adae.write_csv("/app/output/adae.csv")

solutions/adam-adae-serious-sequence/result.sas

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24 lines
/* opensas reference solution for the yamaa benchmark adam-adae-serious-sequence.   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 AETERM AESER AESTDTC $1024;  infile "/app/input/ae.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;  input STUDYID : $char1024. USUBJID : $char1024. AESEQ AETERM : $char1024. AESER : $char1024. AESTDTC : $char1024.;run;data ordered; set ae; nodate=missing(AESTDTC); run;proc sort data=ordered; by STUDYID USUBJID nodate AESTDTC AESEQ; run;data result;  length ASTDT $10;  set ordered; by STUDYID USUBJID;  if first.USUBJID then counter=0;  ASTDT=AESTDTC;  if AESER='Y' then do; counter+1; SERSEQ=counter; end;run;proc sql;  create table final as select STUDYID, USUBJID, AESEQ, AESER, ASTDT, SERSEQ from result;quit;proc export data=final outfile="/app/output/adae.csv" dbms=csv replace; run;