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: show that a non-finite number (positive infinity, negative infinity, or Not-a-Number, NaN) is stored as missing, whichever of three routes brings it into a column: a value written in the specification, a value read from the source, or a value returned by a study calculation.

Input: one spec.yaml over a subject-level dataset whose numeric fields POSITIVE_INFINITY, NEGATIVE_INFINITY, and NOT_A_NUMBER each hold one non-finite value.

Columns: every one of these is stored as missing.

  • YAML_PINF, YAML_NINF, and YAML_NAN write positive infinity, negative infinity, and NaN directly in spec.yaml.
  • SOURCE_PINF, SOURCE_NINF, and SOURCE_NAN copy them from POSITIVE_INFINITY, NEGATIVE_INFINITY, and NOT_A_NUMBER.
  • FUNCTION_PINF, FUNCTION_NINF, and FUNCTION_NAN take them from the study calculation numeric_constant. Its contract allows a missing result; a calculation that may not return one fails the run when it returns a non-finite value.

Note: a quoted spelling such as '.inf' remains text unless converted to a numeric type.

Engines: numeric_constant belongs to the ordinary installed packages in rust/tests/project-programs. The explicit python/environment.yaml and r/environment.yaml carry equivalent versionless definitions and inline tests. Both return the same three non-finite constants.

Inputs

Under review
1 source file

input/dm.csv

Edit
1 row
input/dm.csv
USUBJIDPOSITIVE_INFINITYNEGATIVE_INFINITYNOT_A_NUMBER
.inf-.Inf.NAN

Expected output

Under review
1 expected row

expected/adsl.csv

Edit
1 row
expected/adsl.csv
USUBJIDYAML_PINFYAML_NINFYAML_NANSOURCE_PINFSOURCE_NINFSOURCE_NANFUNCTION_PINFFUNCTION_NINFFUNCTION_NAN
—————————
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
ADSLUSUBJIDUnique Subject IdentifiercharacterCopy value from DM.USUBJID.Predecessor
ADSLYAML_PINFPositive Infinity YAML LiteralnumericSet constant value "inf".Assigned
ADSLYAML_NINFNegative Infinity YAML LiteralnumericSet constant value "-inf".Assigned
ADSLYAML_NANNot-a-Number YAML LiteralnumericSet constant value "nan".Assigned
ADSLSOURCE_PINFPositive Infinity Source ValuenumericCopy value from DM.POSITIVE_INFINITY.Predecessor
ADSLSOURCE_NINFNegative Infinity Source ValuenumericCopy value from DM.NEGATIVE_INFINITY.Predecessor
ADSLSOURCE_NANNot-a-Number Source ValuenumericCopy value from DM.NOT_A_NUMBER.Predecessor
ADSLFUNCTION_PINFPositive Infinity Function ResultnumericCall project function numeric_constant with kind = positive-infinity.Derived
ADSLFUNCTION_NINFNegative Infinity Function ResultnumericCall project function numeric_constant with kind = negative-infinity.Derived
ADSLFUNCTION_NANNot-a-Number Function ResultnumericCall project function numeric_constant with kind = nan.Derived
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

yamaa code

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

run.R

Edit
4 lines
library(yamaa)adsl <- yamaa_domain("spec.yaml", environment = "r/environment.yaml")$outputadsl

run.py

Edit
4 lines
import yamaaadsl = yamaa.domain("spec.yaml", environment="python/environment.yaml").outputadsl