Manage 'CDISC' 'ADaM' Dataset Specifications in 'YAML' Format

Load, validate, and manipulate Clinical Data Interchange Standards Consortium ('CDISC') Analysis Data Model ('ADaM') dataset metadata stored as 'YAML' files. Metadata files are validated against a JSON schema. Provides functions to inspect and modify columns, parameters, and row-level operations within and across 'ADaM' domains. Designed for use with the 'mighty' framework.

mighty.metadata

Overview

Clinical data scientists programming CDISC Analysis Data Model (ADaM) datasets need specifications that are both human-readable and machine-readable. These specifications typically live in Excel spreadsheets that are hard to version-control, difficult to validate, and cannot be consumed by automation tooling. mighty.metadata replaces that workflow with YAML files.

The package loads YAML specifications, validates them against a JSON schema, and provides a pipe-friendly R interface for inspecting and modifying column, parameter, and row definitions. It works at two levels: a single ADaM domain (mighty_domain()) or an entire study (mighty_study()).

mighty.metadata is part of the mighty framework for automated ADaM programming. It is built on S7 and S7schema for robust validation and modern OOP.

Installation

# Install from CRAN:
install.packages("mighty.metadata")

# Install the development version from GitHub:
pak::pak("NovoNordisk-OpenSource/mighty.metadata")

Usage

Load and inspect a single domain

mighty_domain() loads a YAML metadata file for a single ADaM dataset and validates it against the schema on load.

library(mighty.metadata)

advs <- mighty_domain(
  file = system.file("examples", "advs.yml", package = "mighty.metadata")
)

advs
#> <mighty.metadata::mighty_domain>
#> ADVS: Vital Signs Analysis Dataset
#> Class: BASIC DATA STRUCTURE
#> Keys: USUBJID, PARAMCD, and AVISITN

Use list_columns(), list_parameters(), and list_rows() to see what the specification contains.

list_columns(advs)
#>  [1] "STUDYID"  "USUBJID"  "SAFFL"    "TRTP"     "VISITNUM" "AVISITN" 
#>  [7] "AVISIT"   "PARAMCD"  "PARAM"    "AVAL"     "AVALC"

list_parameters(advs)
#> [1] "BMI"    "BMIGRP"

list_rows(advs)
#> [1] "BASELINE"

Modify a domain

Each dimension (columns, parameters, rows) has a consistent set of verbs: list_*, add_*, remove_*, update_*, select_*, and move_*. These return a modified mighty_domain object and work naturally with the pipe.

# Add a new column
advs |>
  add_column(id = "TRTA", label = "Actual Treatment", .pos = 3) |>
  list_columns()
#>  [1] "STUDYID"  "USUBJID"  "TRTA"     "SAFFL"    "TRTP"     "VISITNUM"
#>  [7] "AVISITN"  "AVISIT"   "PARAMCD"  "PARAM"    "AVAL"     "AVALC"

# Remove columns
advs |>
  remove_columns(c("VISITNUM", "AVALC")) |>
  list_columns()
#> [1] "STUDYID" "USUBJID" "SAFFL"   "TRTP"    "AVISITN" "AVISIT"  "PARAMCD"
#> [8] "PARAM"   "AVAL"

Work with a full study

mighty_study() loads every YAML file from a directory. Each file becomes a named mighty_domain. The optional _study.yml provides study-level properties and _mighty.yml provides mighty framework configuration.

study <- mighty_study(
  path = system.file("examples", package = "mighty.metadata")
)

study
#> <mighty.metadata::mighty_study/list/S7_object>
#> @ mighty: `external_data`
#> @ study: `study_id`
#> $ ADAE: <mighty.metadata::mighty_domain>
#> $ ADSL: <mighty.metadata::mighty_domain>
#> $ ADVS: <mighty.metadata::mighty_domain>

names(study)
#> [1] "ADAE" "ADSL" "ADVS"

str(study@study)
#> List of 1
#>  $ study_id: chr "example_study"
str(study@mighty)
#> List of 1
#>  $ external_data:List of 3
#>   ..$ :List of 2
#>   .. ..$ id  : chr "DM"
#>   .. ..$ keys: chr [1:2] "STUDYID" "USUBJID"
#>   ..$ :List of 2
#>   .. ..$ id  : chr "VS"
#>   .. ..$ keys: chr [1:2] "STUDYID" "USUBJID"
#>   ..$ :List of 2
#>   .. ..$ id  : chr "AE"
#>   .. ..$ keys: chr [1:2] "STUDYID" "USUBJID"

Useful links

• vignette("mighty-metadata") – Getting started guide
• vignette("adam-schema") – Domain YAML schema reference
• vignette("study-schema") – Study YAML schema reference
• vignette("mighty-schema") – Mighty YAML schema reference
• S7schema – Schema validation engine used by mighty.metadata
• Novo Nordisk Open Source R packages – Overview of R packages published by Novo Nordisk.