Expression Forms

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

EditUnder reviewWhat this benchmark means

README.md

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

Goal: pin how each column's value is written and read: fixed values, values copied by naming their source column, age bands, a population flag chosen by a condition, first-available country selection, joining text, and the largest and smallest of several collected weights.

Input: one spec.yaml. DM carries one record per subject: sex, age, first-dose date, country, site country, and two collected weights, any of which may be blank.

Columns:

STUDYID holds a fixed study code written directly in the column.
USUBJID and SEX copy the collected values by naming the source column alone.
AGEGRP places each age in a band: under 18, 18 to 64, or 65 and over. Each band includes its lower edge, so an age of exactly 65 is in the oldest band and an age of exactly 18 is in the middle band; a 12-year-old exercises the youngest band. A subject with no age carries the fallback text NOT REPORTED.
SAFFL flags the safety population: Y for a subject with a first-dose date, N for a subject without one.
COUNTRY takes the subject's own country, or the site country when the subject's own entry is blank. When both are blank it carries the declared fallback text.
SUBJLBL joins the fixed word Subject to the subject number.
WTMAX carries the larger of the two collected weights, skipping a blank one; with both blank it stays blank.
WTMIN carries the smaller of the two collected weights, skipping a blank one; with both blank it stays blank.

YAML specification

1 spec file

spec.yaml

Edit92 lines

1yamaa: "1.0"2type: spec3model: ADaM4domain: ADSL5keys: [USUBJID]6input:7  DM: {path: input/dm.csv, types: {AGE: int, WT_SCR: int, WT_BL: int}}8base: DM910output:11  path: adsl.csv12  columns: [STUDYID, USUBJID, SEX, AGEGRP, SAFFL, COUNTRY, SUBJLBL, WTMAX,13            WTMIN]1415columns:16  # A literal returns its declared value unchanged.17  - name: STUDYID18    type: str19    label: Study Identifier20    derivation: "'YAMAA-01'"2122  # Text that holds only a name is a reference: it reads DM.USUBJID23  # through the canonical source binding.24  - name: USUBJID25    type: str26    label: Unique Subject Identifier27    derivation: DM.USUBJID2829  - name: SEX30    type: str31    label: Sex32    derivation: DM.SEX3334  # Cut assigns a numeric source to one labeled band. Bands are closed on35  # the left, so an age of exactly 65 lands in 65+ and an age of exactly36  # 18 lands in 18-64; a missing age takes the declared missing value.37  - name: AGEGRP38    type: str39    label: Age Group40    derivation:41      cut:42        source: DM.AGE43        breaks: [18, 65]44        labels: ['<18', '18-64', '65+']45        missing: NOT REPORTED4647  # Each then and the trailing otherwise nests an expression. The first48  # true branch wins; a subject with no first-dose date falls through to49  # otherwise.50  - name: SAFFL51    type: str52    label: Safety Population Flag53    derivation:54      case:55        - when: "DM.RFXSTDTC IS NOT NULL"56          then: "'Y'"57        - otherwise: "'N'"5859  # First_available returns the first non-missing source, or the declared60  # missing value when every source is missing.61  - name: COUNTRY62    type: str63    label: Country64    derivation:65      first_available:66        sources: [DM.COUNTRY, DM.SITECNTY]67        missing: UNKNOWN6869  # str_template writes literal text beside a braced variable reference.70  - name: SUBJLBL71    type: str72    label: Subject Label73    derivation:74      str_template: "Subject {DM.SUBJID}"7576  # Greatest returns the largest non-missing source, or missing when none77  # exist. The sources must be mutually comparable.78  - name: WTMAX79    type: int80    label: Highest Recorded Weight81    derivation:82      greatest:83        sources: [DM.WT_SCR, DM.WT_BL]8485  # Least returns the smallest non-missing source, or missing when none86  # exist.87  - name: WTMIN88    type: int89    label: Lowest Recorded Weight90    derivation:91      least:92        sources: [DM.WT_SCR, DM.WT_BL]

Inputs

Under review1 source file

input/dm.csv

Edit6 rows

input/dm.csv
STUDYID	SUBJID	SEX	AGE	RFXSTDTC	COUNTRY	SITECNTY	WT_SCR	WT_BL
YAMAA-01	001	M	65	2026-01-12	USA	USA	70	72
YAMAA-01	002	F	40	2026-01-19	—	CAN	—	68
YAMAA-01	003	M	—	—	—	—	—	—
YAMAA-01	004	F	25	2026-02-02	GBR	GBR	55	55
YAMAA-01	005	F	18	2026-02-10	USA	—	80	75
YAMAA-01	006	M	12	—	GBR	GBR	45	45

Expected output

Under review6 expected rows

expected/adsl.csv

Edit6 rows

expected/adsl.csv
STUDYID	SEX	AGEGRP	SAFFL	COUNTRY	SUBJLBL	WTMAX	WTMIN
YAMAA-01	M	65+	Y	USA	Subject 001	72	70
YAMAA-01	F	18-64	Y	CAN	Subject 002	68	68
YAMAA-01	M	NOT REPORTED	N	UNKNOWN	Subject 003	—	—
YAMAA-01	F	18-64	Y	GBR	Subject 004	55	55
YAMAA-01	F	18-64	Y	USA	Subject 005	80	75
YAMAA-01	M	<18	N	GBR	Subject 006	45	45

Shaded columns are derived— = missing value

Human review and assessment

Dataset Name	Variable Name	Variable Label	Type	Computational Method	Origin
ADSL	STUDYID	Study Identifier	character	Set constant value "YAMAA-01".	Assigned
ADSL	USUBJID	Unique Subject Identifier	character	Copy value from DM.USUBJID.	Predecessor
ADSL	SEX	Sex	character	Copy value from DM.SEX.	Predecessor
ADSL	AGEGRP	Age Group	character	Band DM.AGE at 18, 65 into "<18", "18-64" and "65+", intervals closed on the left; a missing source → "NOT REPORTED".	Derived
ADSL	SAFFL	Safety Population Flag	character	If DM.RFXSTDTC IS NOT NULL then "Y"; otherwise "N".	Derived
ADSL	COUNTRY	Country	character	First non-missing of DM.COUNTRY and DM.SITECNTY; all missing → "UNKNOWN".	Derived
ADSL	SUBJLBL	Subject Label	character	Fill the template "Subject {DM.SUBJID}".	Derived
ADSL	WTMAX	Highest Recorded Weight	numeric	Greatest of DM.WT_SCR and DM.WT_BL.	Derived
ADSL	WTMIN	Lowest Recorded Weight	numeric	Least of DM.WT_SCR and DM.WT_BL.	Derived

Version	Date	Author	Description
1.7	2026-10-20	yamaa docs build	Read 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
1.6	2026-10-17	yamaa docs build	Describe collected date parts written as ISO 8601 date text and times of day counted in seconds since midnight
1.5	2026-10-17	yamaa docs build	Name 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
1.4	2026-10-15	yamaa docs build	State datetime precision codes through the hour and minute, and complete only the time parts a collected datetime lacks
1.3	2026-09-29	yamaa docs build	Show study codelist names, external dictionaries, and ISO 8601 formats in variable rows; list the document's codelist values and metadata
1.2	2026-09-29	yamaa docs build	Classify ADaM copies from a declared input as Predecessor and recodes as Derived, and SDTM recodes of locally derived columns as Derived
1.1	2026-09-29	yamaa docs build	Resolve lookups into the donor record they select, and give every derivation shape a plain-language rule instead of its raw YAML
1.0		yamaa docs build	Initial generation from spec.yaml

R

Edit3 lines

1library(yamaa)23yamaa_spec("spec.yaml")$run()$output

Python

Edit3 lines

1import yamaa23yamaa.spec("spec.yaml").run().output

input/dm.csv

expected/adsl.csv

Your review

Mapping spec

yamaa code

R

Python