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admiral

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ADaM in R Asset Library

Explore all the other packages in the {admiral} ecosystem to learn more about ADaM programming in R.

Purpose

To provide an open source, modularized toolbox that enables the pharmaceutical programming community to develop ADaM datasets in R.

Installation

The package is available from CRAN and can be installed with:

install.packages("admiral")

To install the development version of the package from GitHub run:

pak::pkg_install("pharmaverse/admiral", dependencies = TRUE)

Cheat Sheet

Admiral Cheat Sheet Image

Release Schedule

The admiral family has several downstream and upstream dependencies and so releases are done in two Phases:

NB: We strive for a regular 6 month release schedule for {admiraldev}, {pharmaversesdtm}, and {admiral}. Extension packages releases are on a content-basis and as such may be more infrequent than the below schedule shows, or there may even be ad-hoc releases.

Release Schedule Phase 1- Date and Packages Phase 2- Date and Packages
Q4 2026/Q1 2027 Late December 2026/Early January 2027 Mid-January 2027
{pharmaversesdtm} {admiralonco}
{admiraldev} {admiralophtha}
{admiral} {admiralvaccine}
{admiralpeds}
{admiralmetabolic}
{admiralneuro}
{pharmaverseadam}
Q2 2027 Mid-June 2027 End of June 2027
{pharmaversesdtm} {admiralonco}
{admiraldev} {admiralophtha}
{admiral} {admiralvaccine}
{admiralpeds}
{admiralmetabolic}
{admiralneuro}
{pharmaverseadam}

Main Goal

Provide users with an open source, modularized toolbox with which to create ADaM datasets in R. As opposed to a “run one line and an ADaM appears” black-box solution or an attempt to automate ADaM.

One of the key aspects of admiral is its development by the users for the users. It gives an entry point for all to collaborate, co-create and contribute to a harmonized approach of developing ADaMs in R across the pharmaceutical industry.

Scope

To set expectations: It is not our target that admiral will ever provide all possible solutions for all ADaM datasets outside of study specific needs. It depends on the user’s collaboration and contribution to help grow over time to an asset library that is robust, easy to use and has an across-industry focus. We do not see a coverage of 100% of all ADaM derivations as ever achievable—ADaM is endless.

We provide:

  • A toolbox of re-usable functions and utilities to create ADaM datasets using R scripts in a modular manner (an “opinionated” design strategy).
  • Pharmaceutical communities and companies are encouraged to contribute to admiral following the provided programming strategy and modular approach.
  • Functions that are comprehensively documented and tested, including example calls—these are all listed in the Reference section.
  • Vignettes on how to create ADSL, BDS and OCCDS datasets, including example scripts.
  • Vignettes for ADaM dataset specific functionality (i.e. dictionary coding, date imputation, SMQs …).

The {admiral} Family of Packages

There are three types of packages in the admiral family:

  • Core package—one package containing all core functions required to create ADaMs, usable by any company (i.e. general derivations, utility functions and checks for ADSL, OCCDS and BDS).
  • TA (Therapeutic Area) package extensions—one package per TA with functions that are specific to algorithms and requirements for that particular TA (e.g. {admiralonco}).
  • Company package extensions—specific needs and plug-ins for the company, such as access to metadata (e.g. {admiralroche} or {admiralgsk}).

Related data packages include:

Both these packages are developed by the admiral team, but can used across the pharmaverse as common, open-source test SDTM or ADaM data.

The following packages are also useful when working with ADaM datasets:

  • {metacore} and {metatools}—these enable users to manipulate and work with dataset metadata.
  • {xportr}—this provides functionality to get xpt files ready for transport.

Contact and Further Reading

We use the following for support and communications between user and developer community and for sharing of knowledge and experience:

  • Slack—for informal discussions, Q&A and building our user community. If you don’t have access, use this link to join the pharmaverse Slack workspace.
  • GitHub Issues—for direct feedback, enhancement requests or raising bugs.
  • Pharmaverse Blog—for bite-sized posts showcasing how admiral and other packages in the pharmaverse can be used to realize the vision of full end-to-end Clinical Reporting in R.
  • Presentation Archive—for a full collection of admiral conference presentations over the years.

Acknowledgments

Along with the authors and contributors, thanks to the following people for their work on the package:

Jaxon Abercrombie, Mahdi About, Teckla Akinyi, Anthony Arroyo, Alex Assuied, James Black, Claudia Carlucci, Asha Chakma, Liming Clark, Bill Denney, Kamila Duniec, Alice Ehmann, Romain Francois, G Gayatri, Ania Golab, Alana Harris, Declan Hodges, Solveig Holmgaard, Anthony Howard, Shimeng Huang, Samia Kabi, Leena Khatri, James Kim, John Kirkpatrick, Robin Koeger, Konstantina Koukourikou, Dinakar Kulkarni, Pavan Kumar, Pooja Kumari, Shan Lee, Wenyi Liu, Sadchla Mascary, Iain McCay, Jack McGavigan, Jordanna Morrish, Syed Mubasheer, Kirill Muller, Marco Musca, Thomas Neitmann, Yohann Omnes, Barbara O’Reilly, Dimple Patel, Celine Piraux, Hamza Rahal, Nick Ramirez, Tom Ratford, Sukalpo Saha, Tamara Senior, Sophie Shapcott, Vladyslav Shuliar, Eric Simms, Daniel Sjoberg, Ondrej Slama, Andrew Smith, Daniil Stefonishin, Vignesh Thanikachalam, Michael Thorpe, Steven Ting, Ojesh Upadhyay, Franciszek Walkowiak, Enki Wang, Phillip Webster, Annie Yang, Andrii Yurovskyi, Junze Zhang, Kangjie Zhang, Zelos Zhu