In a large organization, collaboration faces many obstacles. Groups may inadvertently reinvent functionality and expend redundant effort. Siloing may impede aggregation and comparison of results. Analysts may not be aware of potential collaborators. …
R is the dominant language in modern quantitative science, however it is still not widely used in pharma industry. In this talk I will share learnings in building an internal R user community in a large global organization, via efforts including …
The R shiny-based nest framework (previously named teal) has been proven valuable in exploratory settings and supporting strategic decision meetings. To allow more clinical studies to be able to adopt this agile framework in a wider range, we've …
Content delivery in preparation for filing a clinical study report requires robust tooling for quickly and reproducibly compiling analysis of study data. Traditionally, this reproducibility has stemmed from one-time, rigorous validation of a …
At R in Pharma 2018, I gave a workshop and a presentation on analyzing clinical trials data with R. Since then much has happened at Roche/Genentech with regard to analyzing clinical trials data with R our R-based projects got funded in order to …
In Pharmaceutical industry, personalized patient care is about having access to traditional and new data sources including comprehensive diagnostic data, sensor data, real-world data, etc., applying traditional and advanced analytics like machine …
Creating datasets and tables, listings and graphs (TLGs) for analyzing clinical trials data with R, such that in the final stage the code, datasets and TLGs can be submitted to the health authorities, is a multifaceted problem. We have been working …
The open-source analytics community is driving innovation in precompetitive spaces like statistical methodology, reproducibility approaches, visualization techniques, and scaling strategies. The diverse and rapdily evolving ecosystem of open-source …
R is a very powerful tool for performing statistical programming, but has had a lower uptake in the life sciences when compared to SAS. As a result, many of the packages created for R are not focused on the type of tasks Statistical Programmers do. …