This webinar is free to both Members of PSI and Non-Members.
\nTo register for this event\, please click here. \;
\nTalks from the speake rs will cover the use of R in a programming community\, submitted to regul ators using R\, and also programming beyond R in C++ and Julia.
\n
\n Ari Siggaard Knoph
\n Speaker \n \n | \n Biography \n | \n Abstract \n |
\n
| Matt Lyon \;studied his Zoolo
gy BSc at the University of Liverpool. His degree included quantitative bi
ology modules which used several statistical data packages. He started his
career at The Francis Crick Institute in London as an Animal Technician b
efore progressing to CRUK Manchester and finally moved to AstraZeneca in 2
020 as an In-Vivo Scientist. \n \n Matt i s currently the Global Head and his departments&rsquo\; representative for Inclusion and Diversity (I&\;D).He also heads up a small\, internation al cross functional team which focusses on creating and rolling out initia tives across the department. This also includes liaising with other areas of the business to promote AZ as a great place to work. \n \n Matt has taken over the Lead of the Steering Committe e of the community of R users at AstraZeneca- &lsquo\;R@AZ&rsquo\; &ndash\ ; which currently has around 1600 members. Building on his I&\;D and qu antitative biology skills\, he is looking at expanding this community with in AZ and beyond. \n \n \n | \n Building a BiggeR Community of R Use
rs at AstraZeneca |
\n Ari is a Prin cipal Statistical Programmer and International Lead Programmer at Novo Nor disk with a seat on the pharmaverse council. He is a driving force behind the adoption of R in clinical deliverables and submissions at Novo Nordisk . Ari is also the author and maintainer of multiple R packages used in the Novo Nordisk ecosystem. \n | \n Completing a submissio
n and beyond in R \n | |
\n
| \n Daniel Saba nes Bove studied Statistics in LMU Munich and obtained his PhD at the Univ ersity of Zurich for his research work on Bayesian model selection. He sta rted his career in 2013 at Roche as a biostatistician\, then worked at Goo gle as a data scientist from 2018 to 2020 before rejoining Roche. He curre ntly leads the Statistical Engineering team in Roche Pharma Product Develo pment 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 B ayesian Inference: With Applications in Biology and Medicine"\, and is cur rently openstatsware.org (AS A BIOP working group on Software Engineering). \n\; \n | \n R(omeo) and Julia - A Love Story by openstatsw
are For implementing stat istical methods in software\, we recently started trying out the Julia lan guage. "Julia is a high-level\, general-purpose dynamic programming langua ge\, most commonly used for numerical analysis and computational science" (Wikipedia) and as such well suited for statistical applications. I will i ntroduce two successful Julia projects. The first project implements joint models for time-to-event and longitudinal outcomes (see e.g. Kerioui et a l. 2022)\, and is available open source as JointModels.jl. I t uses Turing.jl for MCMC based Bayesian inference\, based on a new distri bution class for time-to-event data specified via hazard functions. The se cond project implements Bayesian safety signal detection as described by B rock et al. (2022)\, under construction and open source as SafetySignalDetection.jl\, again using Turing.jl an d with a suitable extension of the expectation-maximization algorithm for fitting Beta mixtures. I will discuss the reasons why these projects were successful\, and describe how we could easily embed the Julia algorithms i nto an R based overall workflow. Finally\, I will introduce openstatsware.org where a growing community of statistical software engineers comes together to build softwa re packages and develop and share best practices for such. \n\; \n |