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.
We have reached a major milestone. The R package riskmetric has been released and is now available on CRAN. What is riskmetric? riskmetric is a collection of risk metrics to evaluate the quality of R packages following the framework suggested by the R validation hub (see our white paper for details). Various quality metrics are provided which evaluate best practices of software development, code documentation, community engagement and development sustainability.
The new year is starting with some good news for the R validation hub. We will be able to present our work at a number of different meetings in 2021: National Institute of Statistical Sciences (NISS; April 21, 2021): NISS-Merck Meet-Up on Open Source Software in Pharma Annual Meeting of Statisticians in the Pharmaceutical Industry (PSI; June 21-23, 2021): Contributed session accepted Joint Statistical Meetings (JSM; August 7-12, 2021): Topic-contributed session accepted
2020 was a busy year for the R Validation Hub. We released our white paper describing our current thinking on a risk based approach to using R for regulatory work. We started to support the implementation of the white paper with tools such as riskmetric and our risk assessment application. And we started a new sub-team with the aim of producing a follow-up white paper on testing. Throughout, we have continued to share and gain feedback on our proposed approach, presenting at User!
Background rOpenSci is an organization devoted to “transforming science through data, software and reproducibility.” One of rOpenSci’s focal activities is peer review of R packages, historically focusing on packages that cover the data management lifecycle. This has historically excluded software implementing statistical methods, for which standards and review require addressing a different set of challenges. This year, we have begun tackling these so as to expand our peer review system to explicitly encompass statistical software, under project funded by the Alfred P.
Background Towards the end of 2019, the R Validation Hub received an additional grant from the R Consortium to progress the next phase of our road map and produce a risk assessment app to complement the riskmetric package. In early 2020, Fission Labs were selected as our partner to build the first iteration of the application. Fission Labs is a software product development services company delivering product life-cycle management and high-end scalable technology solutions.
It’s time to bring some updates to you on the current status of the R validation hub and we are have plenty of great developments. Fission has finished their work on the R package risk assessment app and made the source code is available on github. It is an interactive web application providing a front end for the collection of metrics for R packages via riskmetric package including visualizations and comparison metrics.
Many contributed R packages lack documentation expected in software qualification, which is required within pharma and other regulated industries. For pharma, there are various regulations, which require documentation that demonstrates software is used appropriately and works as expected. Thus, industry needs to establish appropriate requirements for R packages using selected metadata and useful risk metrics. In context of the R Validation Hub, the R package riskmetric has been developed, which seeks to take the first steps in identifying metrics and best practices to quantify the quality of R packages.
In these uncertain times, we would like to provide you with some good news and update you on the progress from the R validation hub. It has been a little while since you heard from us, but that doesn’t mean we were less active. Communication: You may have realized that there are no further re-occurring meetings scheduled for the R validation hub since February. Following previous discussions, we are planning regular update releases via the website and newsletters instead.
A Risk-based Approach for Assessing R package Accuracy within a Validated Infrastructure: White Paper Summary
In this article I summarise some of the key themes from the R Validation Hub’s white paper, A Risk-based Approach for Assessing R package Accuracy within a Validated Infrastructure. What is the R Validation Hub? The R Validation Hub is a cross-industry initiative whose mission is to enable the use of R by the Bio-Pharmaceutical Industry in a regulatory setting, where the output may be used in submissions to regulatory agencies.