![]() ![]() Because of the availability and extensibility of open-source alternatives, such as R and Python, the speed at which these latter two languages have evolved to meet the statistical and data science needs of the user community has far outpaced SAS in recent years. However, SAS is a closed-source language, it is not extensible, and it requires licensing. This is because both the expertise and infrastructure within the industry have long been SAS-based. A rift appears in the biostatistics workforceĪlthough FDA has long held to the principle of language independence, SAS has until recently been the predominant statistical computing language used for study submissions. Novel, flexible statistical computing environments (SCEs) can solve the unique challenges that biostatistics leaders face in enabling diverse teams to effectively collaborate and accelerate study submissions. ![]() However, having all SAS programmers proficient in R or Python may not be necessary to take advantage of all the functionality that these languages offer. The prevailing approach has been to get established Statistical Analysis System (SAS) programmers proficient in R and eventually in Python, two open-source programming languages that are constantly evolving due to thriving communities of active contributors who are continually expanding the languages’ toolsets. As companies look to accelerate R&D, there has been an increasing focus on clinical development, where the availability of efficiency-enabling technology is currently outpacing workforce enablement.īiostatistics leaders, in particular, are aiming to modernize the skillset of their current statistical programming organizations. In the biomedical and pharmaceutical industries, speed to market of safe and effective new therapies is everything-both for patients who need them and for the life sciences companies that develop them. ![]()
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