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What is admiral?
  • Think of admiral as a toolbox of modular blocks (R functions) to create analysis derivations:
    • each block has a stand alone purpose (each function provides a specific functionality)
    • Data Scientists can create their own blocks (create own R functions)
  • Constructing ADaM dataset should become like building out of blocks that are based on admiral modular functions and user created modular functions.
Why did we decide to start admiral?
  • Data analysis challenges in clinical trials vary depending on scientific goals, therapeutic areas, indications, data sources and data quality. We all face the same challenge so why limit ourselves only to company-level adoption and crowd-sourcing to create ADaM datasets?
  • Build ADaMs via collaboration and co-creation
  • Early engagement with other like-minded companies moving towards R could lead to our solution being shared open source as a framework for contribution across-industry
  • Building ADaMs like a modular building blocks, everyone can contribute and each module has a clear input and output to enable re-usable solutions
  • Users can “slot in” their own modules to address specific company/TA/Molecule/Study requirements
  • TA specific requirements can be open sourced again and transformed into a common ADaM approach for such analysis
  • the long-term gain of a consistent way of producing ADaM and a wider community of across-industry developers contributing to grow the codebase to cover the infinite array of possibilities
  • Contributors: An option to make a name for yourself in the Pharma open-source community & an avenue to collaborate with other like-minded people across the industry
  • Imagine if ADaMs are built in a consistent manner with the same code from openly maintained functions and its impact on the Health Authorities, readable code, QC, talent flow
Why did we use R as a programming language?
  • R is not an isolated software product, everyone can contribute (open source principal)
  • People from University/Statistical talent pipeline more likely to come through with R skills rather than a proprietary language
  • There seems to be a strong data science/analytics R community
  • FDA open to accepting R submissions and are heavy users themselves
  • Top of the line visualization/graphics - R-Shiny for interactive data displays and also R Markdown offers great report writing functionality
  • R is very popular among statisticians so new statistical methods are likely implemented in R before any other language
  • There might be equally suited programming languages out there - however at some stage we had to make a decision :)
Why do we use a certain R version and package versions for development?
  • The choice of R Version is not set in stone. However, a common development environment is important to establish when working across multiple companies and multiple developers. We currently work in R Version 3.6.3, but that will change as we move forward with admiral. This need for a common development environment also carries over for our choice of package versions.
  • GitHub allows us through the Actions/Workflows to test admiral under several versions of R as well as several versions of dependent R packages needed for admiral. Currently we test admiral against R Version 3.6.3 with a CRAN package snapshot from 2020-02-29, R Version 4.0 with a CRAN package snapshot from 2021-03-31 and the latest R version with the latest snapshots of packages. You can view this workflow and others on our admiralci GitHub Repository.
  • This common development allows us to easily re-create bugs and provide solutions to each other issues that developers will encounter.
  • Reviewers of Pull Requests when running code will know that their environment is identical to the initiator of the Pull Request. This ensures faster review times and higher quality Pull Request reviews.
  • We achieve this common development environment by using a lockfile created from the renv package. New developers will encounter a suggested renv::restore() in the console to revert or move forward your R version and package versions.
Admiral offers a toolbox of functions to facilitate ADaM. What does that mean?
  • Functions are usually not necessarily specific but parameter driven:
    • e.g. the derive_vars_aage has a parameterized start and end-date and a unit.
    • Depending on the parameters results may vary as does the specification.
    • Functions serve as a toolbox so the user can create their ADaM according to the requirements.
    • The principles, programming strategy and documentation of admiral are considered as a framework for users to contribute.
How does a user know what a function does exactly?
  • Function details and its purpose, the requirements, parameters, dependencies and examples are documented in the header of each function.
  • Complex functions potentially have a vignette on the admiral homepage to provide more details.
  • admiral does not provide a link to an explicit specification in the define.xml.
Would {admiral} create a whole ADaM dataset?
  • admiral is meant as a toolbox to enable Data Scientists to build ADaMs according to their varying analysis needs
  • admiral is not meant as a “click a button, out comes your ADaM” tool
  • on the admiral webpage, example scripts are provided which can be used as a starting point to create an ADaM (see at the end of a vignette)
In which order does a user need to execute the functions?
  • Guidance will be provided for ADSL, BDS and OCCDS ADaM structure including template scripts.
Is the {admiral} package validated?
  • All functions are reviewed and tested (see What will be provided around function testing?) to ensure that they work as described in the documentation.
  • Test cases for each function will be part of the R package.
  • Users can add to the tests or provide additional feedback.
  • The testing the admiral team will do for each function does not replace the QC and validation process at each company.
  • A GitHub action (using open source packages) exists to generate a validation report for an R package, which would be an option for any company to use. An example report using an earlier version of admiral exists here as an illustration.
What will be provided around function testing?
  • Unit tests for reliability of each function - available as part of open source release
  • Some integration testing will be done to ensure functions can be called together to create ADaM (e.g. even via the internal testing teams)
  • Guidance for testing and documentation expectations of community contribution functions. Then it is for each company to cover the following:
    • validation to be able to use the package on company-specific SCE for GxP purposes and associated audit evidence
    • strategy of how the use of admiral fits into company-specific quality assurance process (double programming comparison versus your company-specific legacy ADaM solution could be appropriate until confidence builds)
    • see our guidance on unit testing
Will admiral provide harmonized define.xml or submittable specifications for functions?
  • No. The functions are documented via programming headers, the define.xml is the responsibility of the end user.
  • Functions are usually generalized and not specific. (see Admiral offers a toolbox of functions to facilitate ADaM. What does that mean?)
  • The users are responsible to make sure they use the functions and their parameters in the right way to ensure alignment with their define.xml
Will {admiral} provide ADaM IG CDISC compliant datasets?
  • Although admiral follows CDISC standards it does not claim that the dataset resulting from calling admiral functions is ADaM compliant. This has to be ensured by the user.
How much of the ADaM IG is covered by admiral?
  • ADaM IG is a standard framework without a specific number of datasets or variables, so it cannot be used as a specific baseline to answer that question.
  • We will provide guidance for each ADaM dataset structure (ADSL, OCCDS and BDS) that will highlight which functionality admiral covers. (see In which order does a user need to execute the functions?)
  • The guidance will also highlight the gaps to be filled by the user (e.g. timing, ranges).
  • For standard ADaM datasets (ADAE, ADCM, …) we can provide an estimated coverage based on early adopters Roche/GSK ADaM implementation
Will there be a user/contribution guide?
How has {admiral} been tested externally to Roche/GSK?
  • During Sept/Oct 2021, a limited release testing was conducted with 18 other companies (and >50 individuals) in order to assess compatibility of the admiral toolkit with different company standards implementations and to test the usability of the functions, e.g. clarity, reliability, robustness, and flexibility.
  • This foundational version of admiral achieved a 7.9 / 10 average score from all the survey respondents and >75% said they’d advocate using admiral for ADaM transformations in R.
  • Some tester quotes:
    • “Extremely easy to learn and get into, well thought and planned. Plenty of minor functions instead of aiming to create a large”jack of all trades" framework. The toolkit does not attempt to become a large one-button ADaM generator (which is fantastic)."
    • “It is a huge advantage for all Pharma companies that we have common functions for common stuff we develop. It will be easier for the authorities when it is the same foundation in the ADaM programs. The development goes faster for every one when we develop across companies, and bug-fixing is faster as many are using same package and will most likely find potential bugs.”
    • “I am a huge proponent of shared solutions within Pharma. Overall I was VERY impressed with the admiral project – both the design, development, documentation, and validation details are available for teams to readily adopt.”