In the Life Science Industry, there has been activity over the last few years to embrace Open-Source languages (mainly R) for performing activities which have traditionally been done in SAS. This talk will focus on effective strategies for teaching statistical programmers with experience using SAS in their daily work (SDTM/ADaM/TFL, etc.) how to learn and transition to R. This strategy consists of four key components. The first focuses on the statistical programmer’s prior experience with R–ranging from no exposure at all, to having used it before entering the industry, to maintaining ongoing familiarity. The second component considers the environmental resources available within the company, such as existing R learning materials and whether generative AI tools have been introduced to support the learning process. The Third component reviews the timing of R-related tasks and works backward to determine which aspect to focus on–basic syntax, pharmaverse packages, or task-specific skills. This helps prevent both knowledge decay from inactivity and overload that hinders learning. The final component involves practical exercises where learners reproduce deliverables previously generated in SAS using R. This approach helps bridge the gap between R syntax and practical application, while fostering a sense of achievement through successful replication.
KuenHung Lin
IQVIA
Strategically Assisting Statistical programmers To succeed in R (SAS2R)
In the Life Science Industry, there has been activity over the last few years to embrace Open-Source languages (mainly R) for performing activities which have traditionally been done in SAS. This talk will focus on effective strategies for teaching statistical programmers with experience using SAS in their daily work (SDTM/ADaM/TFL, etc.) how to learn and transition to R. This strategy consists of four key components. The first focuses on the statistical programmer’s prior experience with R–ranging from no exposure at all, to having used it before entering the industry, to maintaining ongoing familiarity. The second component considers the environmental resources available within the company, such as existing R learning materials and whether generative AI tools have been introduced to support the learning process. The Third component reviews the timing of R-related tasks and works backward to determine which aspect to focus on–basic syntax, pharmaverse packages, or task-specific skills. This helps prevent both knowledge decay from inactivity and overload that hinders learning. The final component involves practical exercises where learners reproduce deliverables previously generated in SAS using R. This approach helps bridge the gap between R syntax and practical application, while fostering a sense of achievement through successful replication.