R/Pharma 2023 Keynotes
R/Pharma 2023 Keynotes
R/Pharma is excited to announce our keynote speakers for 2023.
Our Impact in the Evolving Data Landscape
Data sources and the volume of data available for driving discovery and informing decisions have substantially increased over time. This increase has resulted in an evolving data and regulatory landscape ripe for the expertise of statisticians and data scientists. Statisticians and data scientists must play a key role to ensure the appropriate use of data and soundness of conclusions reached from analyses of the data. In this talk, we will explore the landscape identifying challenges and opportunities and highlight our contributions and impact.
Dionne Price is the Deputy Director of the Office of Biostatistics in the Office of Translational Sciences, Center for Drug Evaluation and Research, FDA. In this role, Dr. Price provides leadership to statisticians involved in the development and application of methodology used in the regulation of drug products. She currently leads cross-cutting, collaborative efforts across FDA to advance and facilitate the use of innovative trial designs in pharmaceutical drug development. Dr. Price received her MS in Biostatistics from the University of North Carolina at Chapel Hill and a PhD in Biostatistics from Emory University. Dr. Price is an active member of the American Statistical Association (ASA) and the Eastern North American Region of the International Biometrics Society. She is a Fellow of the ASA and the 2023 President of the ASA.
The importance of the SCE in enabling our shift from proprietary programming to open-source data science
Historically building a great SCE for clinical reporting involved selecting a vendor, integrating their product, and supporting a single proprietary language. The shift to report clinical trials using R has had a much broader impact than just swapping out a language, with it also catalysing the adoption of data science in statistical programming. For the team building the latest generation of SCEs, this has led to a complex eco-system of dynamic dependencies to enable reproducible research, the need to adapt to a much faster pace of development of the tools used, and facilitated bringing different elements of evidence generation like trial design, and real world evidence, to co-exist with statistical programming. During this talk, we’ll discuss this evolution, the underlying tensions we continue to tackle aspiring to balance innovation against business continuity, and the critical role SCE architecture plays facilitating a shift to data science.
James Black gained his PhD from Cambridge University, with a thesis focussed on analysing the effect of randomising type 2 diabetes patients to different CVD risk management guidelines. After grad school he joined Roche’s real world data team, where he was a key driver in their rapid shift from using SAS and file shares to R, databases and git. Currently he leads Insights Engineering in Roche Pharma Product Development, and is the Product Owner for the PHC/RWE and Clinical Trial Reporting Scientific Computing Environments. He is also involved in Open Source and industry collaborations, sitting on the board of Open Source in Pharma, the R Consortium Pharma Oversight working group, PhUSE’s SCE Council and is the Product Development representative for Roche’s internal Open and Inner Source office.
Daniel Sabanés Bové
Why we Need to Improve Software Engineering in Biostatistics - A Call to Action
Programming is ubiquitous in applied biostatistics, and most statisticians know a programming language such as R - yet software engineering is still neglected as a skill and undervalued as a profession in pharmaceutical statistics. Why is this a problem? Importantly, we run the risk of wrong decisions when relying on code that we wrote ourselves without any code review by other statisticians. When transitioning over undocumented code to successors or other teams, we cannot be sure that they can even use, yet maintain it in the future at all. Also, whether they can reproduce results we produced earlier is a matter of luck. If we later need to add features to our code, and don’t have sufficient tests in place, we will undoubtedly introduce bugs and alter the program behavior without knowing it. Finally, if we need to implement new statistical methods for analyses submitted to regulators, we need to have appropriate software validation pipelines in place, which will demand well developed and tested code. What can we do about it? First and foremost, we must become aware of the problem. Second, we need to take software engineering seriously, starting from education in basic software engineering skills - across schools, universities, and during the work life. Establishing dedicated software engineering teams within academic institutions and companies can be a key factor for the establishment of good software engineering practices and catalyze improvements across research projects. Providing attractive career paths is important for the retainment of talents. Finally, collaboration between software developers from different organizations is key to harness open-source software efficiently and optimally, while building trusted solutions. We illustrate the potential with examples of successful projects.
Daniel Sabanes Bove studied Statistics in LMU Munich and obtained his PhD at the University of Zurich for his research work on Bayesian model selection. He started his career in 2013 at Roche as a biostatistician, then worked at Google as a data scientist from 2018 to 2020 before rejoining Roche. He is currently leading the Statistical Engineering team in Roche Pharma Product Development that works on productionizing R packages, Shiny modules and how-to templates for data scientists. Daniel is co-author of multiple R packages published on CRAN and Bioconductor, as well as the book “Likelihood and Bayesian Inference: With Applications in Biology and Medicine”, and is currently co-chairing openstatsware, a working group focusing on Software Engineering in biostatistics.