Description:
Writing statistical software that can robustly implement complex methods is not trivial, but it is a lot of fun, too. In this workshop we will show practical steps how you can start from a statistical method described in the literature and arrive at a production grade R package. We illustrate this with a case study of the recently developed {mmrm} package for analyzing repeated measures.
Instructors:
Dr. Daniel Sabanés Bové
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.
Doug Kelkhoff
Doug Kelkhoff is a Principal Data Scientist at Roche where he has bounced between statistical and engineering roles over his six year tenure. He has supported multiple trials, infrastructure pilots and has been a main-stay at the R Validation Hub advocating for the adoption of R packages for regulatory-ready analysis. Doug is a champion of the embedded use of open source tools and steering the pharmaceutical industry toward a more collaborative engagement with open source developers. It's with this spirit at heart that Doug contributes to such tools as the OpenPharma {mmrm} package. We're excited to share our experiences and lesson learned while co-developing the mmrm package as an example of successful cross-industry open-source development.
