synadam

Generate synthetic ADaM (Analysis Data Model) datasets from real clinical trial data. synadam preserves the structure of real ADaM datasets (column names, relationships, ranges) while removing identifiable patient information — enabling development and testing of analysis pipelines without access to real data.

Key Features

• Privacy-preserving: Synthetic data maintains structure without exposing identifiable patient information.
• Multiple dataset types: Supports ADSL, BDS (e.g. ADLB), OCCDS (e.g. ADAE), and TTE (e.g. ADTTE).
• Auto-configuration: generate_study_config() scans your ADaM directory and infers dataset types and column roles automatically.
• Reproducible: Deterministic output via random seeds.

Installation

Install the CRAN release version:

install.packages("synadam")

Or install the latest, development version from GitHub:

# install.packages("remotes")
# remotes::install_github("Novartis/synadam")
install.packages("synadam", repos = c('https://novartis.r-universe.dev', 'https://cloud.r-project.org'))

Quick Start

library(synadam)

# 1. Auto-generate a YAML config from your ADaM directory. The config is
#    written into output_dir, alongside where the synthetic data will land.
yaml_path <- generate_study_config(
 adam_dir   = "/path/to/adam_data/",
 output_dir = "./syn_data",
 seed       = 42
)

# 2. Review the generated YAML, then simulate
simulate_study(yaml_path)

Synthetic datasets are saved as individual .rds files (e.g., syn_adsl.rds, syn_adlb.rds) in the output directory.

Please visit the documentation site for more details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

License

This package is licensed under the MIT License. See LICENSE for details.