The value that R brings to clinical reporting is well established, and this has been the key factor in securing R's position within the pharma industry. However, as effective data collection, curation, and aggregation have increased across functions within the pharma industry over the past years, real opportunities have emerged to leverage R's ecosystem across the development pipeline. For the past five years I have been working as a data scientist supporting late-stage process development teams, and I'll use this talk to describe some successful applications of R to common process development challenges in data analysis and reporting, as well as some areas where further improvement is needed.
Nick Kaiser
Novartis
Enhancing Late-Stage Process Development with R
The value that R brings to clinical reporting is well established, and this has been the key factor in securing R's position within the pharma industry. However, as effective data collection, curation, and aggregation have increased across functions within the pharma industry over the past years, real opportunities have emerged to leverage R's ecosystem across the development pipeline. For the past five years I have been working as a data scientist supporting late-stage process development teams, and I'll use this talk to describe some successful applications of R to common process development challenges in data analysis and reporting, as well as some areas where further improvement is needed.