As the R Validation Hub closes in on its 5th year of activity I want to take a quick trip down memory lane and reflect on how we got here. Perhaps I should start by reminding everyone how it all started. Back in 2018, as the prospect of using R for any regulated analysis was still a hotly contested question, industry participants brought our donated space at Harvard to capacity as attendees gathered for the first ever R/Pharma conference.
Welcome! It’s with great excitement and long-awaited anticipation that I get to share some recent updates that have hit the {riskassessment} app’s GitHub repository earlier this month. If this is the first time you’ve heard or seen the application, I’d recommend starting with our README to gain some familiarity with the project, especially with installation instructions. However, (in a nut-shell) the app is a full-fledged R package that seeks augment the utility of the {riskmetric} package within an organizational context.
The {riskassessment} app, presented by Aaron Clark from the R Validation Hub Executive Committee, was voted best Shiny app at shinyConf 2023. The 2nd Annual Shiny Conference was held in March 2023. It was all virtual with over 4k global registrants. Congratulations!! The app provides a shiny front-end to augment the utility of the {riskmetric} package, thus user-friendly and interactive access to risk assessment of R packages. The apps functionalities include:
Last year, the R validation hub recently initiated a three-part presentation series on case studies in which eight pharmaceutical companies shared their experiences on building a GxP framework with R. These case studies highlighted both easy and challenging aspects of implementing risk assessment for R packages in a GxP environment. In this blog post we attempt to summarize common themes, difference in approaches, and challenges. All implementations followed the risk validation process for R packages outlined in the white paper.
I’m pleased to share that the R Validation Hub’s efforts have been recognised in the ASA Biopharmaceutical report, Fall 2022. Within the edition you can find our paper, Risk Assessment of R Packages: Learnings and Reflections. This paper reflects on our white paper; provides an overview of our {riskmetric} package and Risk Assessment application; and summarises our 2022 case studies (which you can now find on our Case Studies page).
It’s time to bring another update to you on the current status of the R validation hub: The riskmetric R package has been stable on CRAN. Recent work has focused on structuring “cohort metrics” – metrics that are conditioned on the package library or execution environment available to R. In R, package behaviors are often dependent on the rest of the R installation, and this new feature will help to make metrics more inspectible and reproducible, as well as allowing us to ask new questions like, “What would be the effect of installing a new package into an R environment?
This is a re-post. The original post from the R Consortium can be found here Recent update by Marly Gotti on the Risk Assessment Shiny App. Marly is an executive committee member of the R Validation Hub where she advocates for the use of R within a biopharmaceutical regulatory setting, and Senior Data Scientist at Biogen. The Risk Assessment App is an interactive web application serving as a front end application for the riskmetric R package.
There is a large variety of contributed R packages, which can be overwhelming when performing their accuracy assessment. These packages can be developed by anyone and may differ in accuracy. The white paper mentions the possibility to define “trusted resources” to simplify the assessment for some of the R packages. The idea follows vendor assessments / audits to explore the internal validation practices of the vendor for proprietary software. For open-source software such audits are not logistically feasible.
Our favorite meeting of the year is approaching: R/Pharma 2021 will be held virtually November 2-4th, 2021. Also watch out for workshops the week before. Since the R validation Hub is closely associated with R/Pharma, we would like to highlight some of the presentations inspired by the R validation Hub, partner initiatives, or generally related to the topic of validation. Tuesday: November 2, 2021 11:00am “R Package Validation at Roche” by Coline Zeballos, Roche 11:20am “Statistical Analysis and Pathway to a Risk-based Assessment of R packages at Merck KGaA/EMD Serono” by Juliane Manitz, EMD Serono 1:50 PM “Panel Discussion – Validation” with representatives of the R validation Hub” Wednesday: November 3, 2021
TransCelerate has published “Modernization of statistical analytics (MSA) Framework”. With goals similar to the R Validation Hub, the TransCelerate MSA framework seeks to demonstrate software reliability by establishing principles of accuracy, traceability, and reproducibility for a modern analytical software environment. The MSA framework is centered around risk-assessment and mitigation practices to demonstrate reliability of software. This framework suggests assessing the accuracy of a software library via a confidence measure built on risk metrics such as published source code, issue management, usage, maturity, etc.
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