Review ask

As a clinical trial statistician or programmer, answer two short questions in Your review: the task and the inputs and outputs. Agreeing with both approves this benchmark; disagreeing with either needs a comment. Propose a fix with the Edit button on any source file, or add it to your comments.

We suggest spending no more than 5 minutes on a review. The other tabs beside Your review are background for assessment, not under review.

Summary

Lifecycle: draftEditUnder review
What this benchmark means
README.md

Public entries: Python and R. Passing coverage requires an installed inventory.

Goal: bound the rows a filter matches, by count and by share: upper and lower count bounds, upper and lower share bounds, per-group share bounds with an inclusive edge, and a deliberately violated warning.

Input: eight subjects in two arms of four. Each has an ARM, a FLAG marking it for review, and a collected VAL. Three are flagged (one in arm A, two in arm B); seven carry a value and one does not.

Checks:

  • Flagged subjects stay at or under one half: 3 of 8 do, so the check holds.
  • Subjects with a collected value stay above three quarters: 7 of 8 do, so the check holds.
  • Per arm, flagged subjects stay at or under one half: arm A has 1 of 4, arm B has 2 of 4, and the exact half counts as held.
  • Flagged subjects number at most three: exactly 3 do, so the check holds and pins the inclusive count boundary.
  • Subjects with a collected value number at least seven: exactly 7 do, so the check holds and pins the inclusive count boundary.
  • Unflagged subjects stay at or under one half: 5 of 8 do not, so the check is violated. It is a warning, so the run still succeeds and the run report carries the broken check.

The run report (adsl-checks.csv) has one row per item the run handled, each naming the dataset (ARTIFACT), the kind of item (KIND), where the spec states it (SPEC_PATH) and its subject (NAME): the run and its outcome (OUTCOME), the subjects read and built, each output column with its missing values and longest text, then one row per check in the order the run evaluated them. A check row carries its label (ID), the rule it applies (RULE), its SEVERITY, and how many units failed out of how many it evaluated (COUNT, TOTAL): the group check evaluates two groups and the others the whole dataset as one unit. Every check covers the dataset rather than one column, so none names a column (TARGET). A held check leaves CONDITION and KEYS empty, reports zero failures and has empty details ({}). The violated warning names its condition, reports in DETAILS how many rows matched out of how many, and lists one empty key ([{}]): a check over the whole dataset has no offending rows.

Inputs

Under review
1 source file

input/dm.csv

Edit
8 rows
input/dm.csv
USUBJIDARMFLAGVAL
AY12
AN10
AN—
AN9
BY11
BY8
BN7
BN6

Expected output

Under review
21 expected rows

expected/adsl-checks.csv

Edit
13 rows
expected/adsl-checks.csv
ARTIFACTKINDSPEC_PATHNAMEIDTARGETRULESEVERITYOUTCOMECONDITIONCOUNTTOTALKEYSDETAILS
adsl.csvrunoutput—————succeeded—8——{}
adsl.csvinputinput.DMDM——————8——{"bytes":103,"path":"input/dm.csv","profile":"csv"}
adsl.csvrowsbaseDM——————88—{}
adsl.csvcolumncolumns.USUBJIDUSUBJID——————08—{"max_length":3}
adsl.csvcolumncolumns.ARMARM——————08—{"max_length":1}
adsl.csvcolumncolumns.FLAGFLAG——————08—{"max_length":1}
adsl.csvcolumncolumns.VALVAL——————18—{}
adsl.csvcheckverifications[0].row_countrow_countflagged-share-within-limit—CHECK-13errorheld—01—{}
adsl.csvcheckverifications[1].row_countrow_countvalues-mostly-present—CHECK-13errorheld—01—{}
adsl.csvcheckverifications[2].row_countrow_countflagged-share-by-arm—CHECK-13errorheld—02—{}
adsl.csvcheckverifications[3].row_countrow_countflagged-count-within-limit—CHECK-13errorheld—01—{}
adsl.csvcheckverifications[4].row_countrow_countvalues-count-floor—CHECK-13errorheld—01—{}
adsl.csvcheckverifications[5].row_countrow_countunflagged-share-warning—CHECK-13warningviolatedrow_count_failed11[{}]{"count":5,"denominator":8}

expected/adsl.csv

Edit
8 rows
expected/adsl.csv
USUBJIDARMFLAGVAL
AY12
AN10
AN—
AN9
BY11
BY8
BN7
BN6
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
ADSLARMPlanned ArmcharacterCopy value from DM.ARM.Predecessor
ADSLFLAGFlagged for ReviewcharacterCopy value from DM.FLAG.Predecessor
ADSLVALCollected ValuenumericCopy value from DM.VAL.Predecessor
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

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