Clinical Site Data at Site Level

1library(yamaa)23clinsite <- yamaa_domain("spec.yaml")$output4clinsite

1import yamaa23clinsite = yamaa.yamaa_domain("spec.yaml").output4clinsite

1# Reference solution for the yamaa benchmark bimo-clinsite-site-level (R track).2#3# Written from the benchmark's full prompt and its inputs alone, as an agent4# would write it. Harbor's oracle agent runs it to check that the prompt can5# be solved and that the grader scores a correct answer 1.67library(dplyr, warn.conflicts = FALSE)8library(readr)910subj <- read_csv(11  "/app/input/adsl.csv",12  col_types = cols(.default = col_character())13)1415clinsite <- subj |>16  # One record per site; the site key stays text so leading zeros survive.17  group_by(STUDYID, SITENUM) |>18  summarise(19    # The alphabetically first arm among the site's treated subjects.20    # A blank (missing) SAFFL counts as not "Y" so it can neither enter21    # the treated arm list nor turn the treated count missing.22    ARM = {23      treated <- !is.na(SAFFL) & SAFFL == "Y"24      treated_arms <- ARM[treated]25      if (length(treated_arms)) min(treated_arms) else NA_character_26    },27    # Treated subjects, then all enrolled subjects, at the site.28    SAFPOP = sum(!is.na(SAFFL) & SAFFL == "Y"),29    ENRLPOP = n(),30    .groups = "drop"31  ) |>32  rename(SITEID = SITENUM) |>33  select(STUDYID, SITEID, ARM, SAFPOP, ENRLPOP) |>34  arrange(SITEID)3536dir.create("/app/output", showWarnings = FALSE)37write_csv(clinsite, "/app/output/clinsite.csv", na = "")

1# Reference solution for the yamaa benchmark bimo-clinsite-site-level (Python track).2#3# Written from the benchmark's full prompt and its inputs alone, as an agent4# would write it. Harbor's oracle agent runs it to check that the prompt can5# be solved and that the grader scores a correct answer 1.67from pathlib import Path89import polars as pl1011subj = pl.read_csv("/app/input/adsl.csv", infer_schema=False, null_values="")1213clinsite = (14    # One record per site; the site key stays text so leading zeros survive.15    subj.group_by("STUDYID", "SITENUM", maintain_order=True)16    .agg(17        # The alphabetically first arm among the site's treated subjects.18        pl.col("ARM").filter(pl.col("SAFFL") == "Y").min().alias("ARM"),19        # Treated subjects, then all enrolled subjects, at the site.20        (pl.col("SAFFL") == "Y").sum().alias("SAFPOP"),21        pl.len().alias("ENRLPOP"),22    )23    .rename({"SITENUM": "SITEID"})24    .select("STUDYID", "SITEID", "ARM", "SAFPOP", "ENRLPOP")25    .sort("SITEID")26)2728Path("/app/output").mkdir(parents=True, exist_ok=True)29clinsite.write_csv("/app/output/clinsite.csv", null_value="")

1/* opensas reference solution for the yamaa benchmark bimo-clinsite-site-level.2   Reads the benchmark inputs and derives the requested output.3   Execute with the existing opensas runtime: opensas /app/output/result.sas. */45data adsl;6  length STUDYID USUBJID SITENUM ARM SAFFL $1024;7  infile "/app/input/adsl.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;8  input STUDYID : $char1024. USUBJID : $char1024. SITENUM : $char1024. ARM : $char1024. SAFFL : $char1024.;9run;1011proc sql;12create table counts as select STUDYID,SITENUM,count(*) as ENRLPOP,sum(case when SAFFL='Y' then 1 else 0 end) as SAFPOP from adsl group by STUDYID,SITENUM;13create table arms as select STUDYID,SITENUM,min(ARM) as ARM from adsl where SAFFL='Y' group by STUDYID,SITENUM;14create table result as select a.STUDYID,a.SITENUM as SITEID,b.ARM,a.SAFPOP,a.ENRLPOP from counts a left join arms b on a.STUDYID=b.STUDYID and a.SITENUM=b.SITENUM;15quit;16proc sql;17  create table final as select STUDYID, SITEID, ARM, SAFPOP, ENRLPOP from result;18quit;19proc export data=final outfile="/app/output/clinsite.csv" dbms=csv replace; run;