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: draftEditUnder review
What this benchmark means
README.md

Goal: for each adverse event, name the subject's most recent earlier serious event (PRIOR_SAEFL, PRIOR_SAEDECOD, PRIOR_SAESEQ), the subject's first serious event (FIRST_SAEDECOD, FIRST_SAESEQ), and the subject's immediately preceding event (PREV_AEDECOD).

Input: adverse event records with study, subject, and sequence (STUDYID, USUBJID, AESEQ), the dictionary term (AEDECOD), and whether the event was serious (AESER).

Variables:

  • PRIOR_SAEFL: Y on a non-serious event when the subject had a serious event with a smaller sequence number, empty otherwise.
  • PRIOR_SAEDECOD / PRIOR_SAESEQ: the term and sequence number of that most recent earlier serious event, empty when there is none.
  • FIRST_SAEDECOD / FIRST_SAESEQ: the term and sequence number of the subject's first serious event, empty when the subject had no serious event.
  • PREV_AEDECOD: the term of the subject's event with the next smaller sequence number, serious or not; empty on the subject's first event, and empty when the preceding event has no coded term.

Note: a serious event with no coded term still counts: the prior flag is Y and its sequence number is named, while the term fields stay empty. The serious events are laid down first, so a serious event's own prior and first serious event fields stay empty, because they cannot look back at records that are still being written. The preceding event is named only after every event is in place, so it can name an event of either kind.

Inputs

Under review
1 source file

input/ae.csv

Edit
14 rows
input/ae.csv
STUDYIDUSUBJIDAESEQAEDECODAESER
YAMAA-011HEADACHEY
YAMAA-012COUGHN
YAMAA-013NAUSEAY
YAMAA-014FATIGUEN
YAMAA-015RASHN
YAMAA-016—Y
YAMAA-017MYALGIAN
YAMAA-011DIZZINESSN
YAMAA-012FEVERY
YAMAA-013VOMITINGN
YAMAA-011BACK PAINN
YAMAA-012INFLUENZAN
YAMAA-011CHEST PAINY
YAMAA-011HYPERTENSIONN

Expected output

Under review
14 expected rows

expected/adae.csv

Edit
14 rows
expected/adae.csv
STUDYIDUSUBJIDAESEQAEDECODAESERPRIOR_SAEFLPRIOR_SAEDECODPRIOR_SAESEQFIRST_SAEDECODFIRST_SAESEQPREV_AEDECOD
YAMAA-011HEADACHEY——————
YAMAA-013NAUSEAY—————COUGH
YAMAA-016—Y—————RASH
YAMAA-012FEVERY—————DIZZINESS
YAMAA-011CHEST PAINY——————
YAMAA-012COUGHNYHEADACHE1HEADACHE1HEADACHE
YAMAA-014FATIGUENYNAUSEA3HEADACHE1NAUSEA
YAMAA-015RASHNYNAUSEA3HEADACHE1FATIGUE
YAMAA-017MYALGIANY—6HEADACHE1—
YAMAA-011DIZZINESSN———FEVER2—
YAMAA-013VOMITINGNYFEVER2FEVER2FEVER
YAMAA-011BACK PAINN——————
YAMAA-012INFLUENZAN—————BACK PAIN
YAMAA-011HYPERTENSIONN——————
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
ADAEAEDECODDictionary-Derived TermcharacterCopy value from AE.AEDECOD.Predecessor
ADAEAESERSerious EventcharacterCopy value from AE.AESER.Predecessor
ADAEPRIOR_SAEFLPrior Serious Event FlagcharacterSet per constructed row; see the Row construction sheet.Derived
ADAEPRIOR_SAEDECODPrior Serious Event Dictionary-Derived TermcharacterSet per constructed row; see the Row construction sheet.Derived
ADAEPRIOR_SAESEQPrior Serious Event Sequence NumbernumericSet per constructed row; see the Row construction sheet.Derived
ADAEFIRST_SAEDECODFirst Serious Event Dictionary-Derived TermcharacterSet per constructed row; see the Row construction sheet.Derived
ADAEFIRST_SAESEQFirst Serious Event Sequence NumbernumericSet per constructed row; see the Row construction sheet.Derived
ADAEPREV_AEDECODPreceding Event Dictionary-Derived TermcharacterTake AEDECOD from the first already-derived row of this dataset matching on STUDYID and USUBJID, where AESEQ < AE.AESEQ, ordered by AESEQ descending, and no match yields blank.Derived
TemplateInclude whenPRIOR_SAEFLPRIOR_SAEDECODPRIOR_SAESEQFIRST_SAEDECODFIRST_SAESEQ
seriousAE.AESER = 'Y'Constant "None"Constant "None"Constant "None"Constant "None"Constant "None"
nonseriousAE.AESER = 'N'If AESEQ IS NOT NULL, with AESEQ taken from the first already-derived row of this dataset matching on STUDYID and USUBJID, where SELF.AESER = 'Y' AND SELF.AESEQ < AE.AESEQ, ordered by SELF.AESEQ descending, and no match yields blank then "Y"; otherwise "None"Copy AEDECOD from the first already-derived row of this dataset matching on STUDYID and USUBJID, where SELF.AESER = 'Y' AND SELF.AESEQ < AE.AESEQ, ordered by SELF.AESEQ descending, and no match yields blankCopy AESEQ from the first already-derived row of this dataset matching on STUDYID and USUBJID, where SELF.AESER = 'Y' AND SELF.AESEQ < AE.AESEQ, ordered by SELF.AESEQ descending, and no match yields blankCopy AEDECOD from the first already-derived row of this dataset matching on STUDYID and USUBJID, where SELF.AESER = 'Y', ordered by SELF.AESEQ, and no match yields blankCopy AESEQ from the first already-derived row of this dataset matching on STUDYID and USUBJID, where SELF.AESER = 'Y', ordered by SELF.AESEQ, and no match yields blank
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-prior-serious-event/full.md

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24 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, AEDECOD, AESER, PRIOR_SAEFL, PRIOR_SAEDECOD, PRIOR_SAESEQ, FIRST_SAEDECOD, FIRST_SAESEQ, PREV_AEDECOD

PRIOR_SAEFL is Y on a non-serious event when the subject had a serious event with a smaller sequence number, and has no value otherwise. PRIOR_SAEDECOD and PRIOR_SAESEQ name the term and sequence number of that most recent earlier serious event, and have no value when there is none. FIRST_SAEDECOD and FIRST_SAESEQ name the term and sequence number of the subject's first serious event, and have no value when the subject had no serious event. A serious event leaves its own prior and first serious event fields empty. A serious event with no coded term still counts: the prior flag is Y and its sequence number is named, while the term fields stay empty.

PREV_AEDECOD names the term of the subject's event with the next smaller sequence number, serious or not. It has no value on the subject's first event, and when the preceding event has no coded term.

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

prompts/adam-adae-prior-serious-event/conventions.md

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11 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, AEDECOD, AESER, PRIOR_SAEFL, PRIOR_SAEDECOD, PRIOR_SAESEQ, FIRST_SAEDECOD, FIRST_SAESEQ, PREV_AEDECOD

PRIOR_SAEFL is Y or has no value.

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

prompts/adam-adae-prior-serious-event/brief.md

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9 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, AEDECOD, AESER, PRIOR_SAEFL, PRIOR_SAEDECOD, PRIOR_SAESEQ, FIRST_SAEDECOD, FIRST_SAESEQ, PREV_AEDECOD

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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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-prior-serious-event/result.R

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91 lines
# Reference solution for the yamaa benchmark adam-adae-prior-serious-event (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()))serious <- ae |>  filter(AESER %in% "Y") |>  arrange(USUBJID, AESEQ)# The subject's first serious event, whatever its term holds.first_sae <- serious |>  distinct(USUBJID, .keep_all = TRUE) |>  select(USUBJID, FIRST_SAEDECOD = AEDECOD, FIRST_SAESEQ = AESEQ)adae <- ae |>  left_join(first_sae, by = "USUBJID") |>  group_by(USUBJID) |>  arrange(AESEQ, .by_group = TRUE) |>  mutate(    # The most recent earlier serious event, by sequence number.    PRIOR_SAESEQ = sapply(      AESEQ,      function(s) {        earlier <- AESEQ[AESER %in% "Y" & AESEQ < s]        if (length(earlier) == 0L) NA_integer_ else max(earlier)      }    ),    PRIOR_SAEDECOD = sapply(      PRIOR_SAESEQ,      function(s) {        if (is.na(s)) NA_character_ else AEDECOD[match(s, AESEQ)]      }    ),    PREV_SEQ = sapply(      AESEQ,      function(s) {        earlier <- AESEQ[AESEQ < s]        if (length(earlier) == 0L) NA_integer_ else max(earlier)      }    ),    PREV_AEDECOD = sapply(      PREV_SEQ,      function(s) {        if (is.na(s)) NA_character_ else AEDECOD[match(s, AESEQ)]      }    ),    PREV_AEDECOD = if_else(      PREV_AEDECOD == "" | is.na(PREV_AEDECOD),      NA_character_,      PREV_AEDECOD    ),    PRIOR_SAEDECOD = if_else(      PRIOR_SAEDECOD == "" | is.na(PRIOR_SAEDECOD),      NA_character_,      PRIOR_SAEDECOD    ),    FIRST_SAEDECOD = if_else(      FIRST_SAEDECOD == "" | is.na(FIRST_SAEDECOD),      NA_character_,      FIRST_SAEDECOD    )  ) |>  ungroup() |>  mutate(    # A serious event leaves its own prior and first fields empty.    PRIOR_SAEFL = if_else(      AESER %in% "N" & !is.na(PRIOR_SAESEQ),      "Y",      NA_character_    ),    PRIOR_SAEDECOD = if_else(AESER %in% "N", PRIOR_SAEDECOD, NA_character_),    PRIOR_SAESEQ = if_else(AESER %in% "N", PRIOR_SAESEQ, NA_integer_),    FIRST_SAEDECOD = if_else(AESER %in% "N", FIRST_SAEDECOD, NA_character_),    FIRST_SAESEQ = if_else(AESER %in% "N", FIRST_SAESEQ, NA_integer_)  ) |>  select(    STUDYID, USUBJID, AESEQ, AEDECOD, AESER, PRIOR_SAEFL, PRIOR_SAEDECOD,    PRIOR_SAESEQ, FIRST_SAEDECOD, FIRST_SAESEQ, PREV_AEDECOD  )dir.create("/app/output", showWarnings = FALSE)write_csv(adae, "/app/output/adae.csv", na = "")

solutions/adam-adae-prior-serious-event/result.py

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72 lines
# Reference solution for the yamaa benchmark adam-adae-prior-serious-event (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))records = []for subject, group in ae.sort(["USUBJID", "AESEQ"]).group_by("USUBJID", maintain_order=True):    rows = group.sort("AESEQ").to_dicts()    serious_seqs = [r["AESEQ"] for r in rows if r["AESER"] == "Y"]    first_seq = min(serious_seqs) if serious_seqs else None    first_term = next(        (r["AEDECOD"] for r in rows if r["AESEQ"] == first_seq), None    )    by_seq = {r["AESEQ"]: r for r in rows}    for row in rows:        seq = row["AESEQ"]        earlier_serious = [s for s in serious_seqs if s < seq]        prior_seq = max(earlier_serious) if earlier_serious else None        prior_term = by_seq.get(prior_seq, {}).get("AEDECOD") if prior_seq else None        earlier_all = [r["AESEQ"] for r in rows if r["AESEQ"] < seq]        prev_seq = max(earlier_all) if earlier_all else None        prev_term = by_seq.get(prev_seq, {}).get("AEDECOD") if prev_seq else None        is_nonserious = row["AESER"] == "N"        # Only an explicit N receives prior and first serious-event fields. A        # serious event with no coded term still counts for the flag and        # the sequence number, while the term fields stay empty.        records.append(            {                "STUDYID": row["STUDYID"],                "USUBJID": row["USUBJID"],                "AESEQ": seq,                "AEDECOD": row["AEDECOD"],                "AESER": row["AESER"],                "PRIOR_SAEFL": None                if not is_nonserious or prior_seq is None                else "Y",                "PRIOR_SAEDECOD": None if not is_nonserious else prior_term,                "PRIOR_SAESEQ": None if not is_nonserious else prior_seq,                "FIRST_SAEDECOD": None if not is_nonserious else first_term,                "FIRST_SAESEQ": None if not is_nonserious else first_seq,                "PREV_AEDECOD": prev_term,            }        )adae = pl.DataFrame(    records,    schema={        "STUDYID": pl.String,        "USUBJID": pl.String,        "AESEQ": pl.Int64,        "AEDECOD": pl.String,        "AESER": pl.String,        "PRIOR_SAEFL": pl.String,        "PRIOR_SAEDECOD": pl.String,        "PRIOR_SAESEQ": pl.Int64,        "FIRST_SAEDECOD": pl.String,        "FIRST_SAESEQ": pl.Int64,        "PREV_AEDECOD": pl.String,    },)Path("/app/output").mkdir(exist_ok=True)adae.write_csv("/app/output/adae.csv")

solutions/adam-adae-prior-serious-event/result.sas

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/* opensas reference solution for the yamaa benchmark adam-adae-prior-serious-event.   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 AESER $1024;  infile "/app/input/ae.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;  input STUDYID : $char1024. USUBJID : $char1024. AESEQ AEDECOD : $char1024. AESER : $char1024.;run;proc sort data=ae(where=(AESER='Y')) out=serious; by STUDYID USUBJID AESEQ; run;data firsts; set serious; by STUDYID USUBJID; if first.USUBJID; run;proc sql; create table joined as select a.*,b.AEDECOD as firstterm,b.AESEQ as firstseq from ae as a left join firsts as b on a.STUDYID=b.STUDYID and a.USUBJID=b.USUBJID; quit;proc sort data=joined; by STUDYID USUBJID AESEQ; run;data result;  length PRIOR_SAEFL $1 PRIOR_SAEDECOD FIRST_SAEDECOD PREV_AEDECOD lastterm prevterm $1024;  retain lastterm lastseq prevterm;  set joined; by STUDYID USUBJID;  if first.USUBJID then do; lastterm=''; lastseq=.; prevterm=''; end;  PREV_AEDECOD=prevterm;  if AESER='N' then do;    PRIOR_SAEDECOD=lastterm; PRIOR_SAESEQ=lastseq;    FIRST_SAEDECOD=firstterm; FIRST_SAESEQ=firstseq;    if not missing(lastseq) then PRIOR_SAEFL='Y';  end;  if AESER='Y' then do; lastterm=AEDECOD; lastseq=AESEQ; end;  prevterm=AEDECOD;run;proc sql;  create table final as select STUDYID, USUBJID, AESEQ, AEDECOD, AESER, PRIOR_SAEFL, PRIOR_SAEDECOD, PRIOR_SAESEQ, FIRST_SAEDECOD, FIRST_SAESEQ, PREV_AEDECOD from result;quit;proc export data=final outfile="/app/output/adae.csv" dbms=csv replace; run;