Historically building a great SCE for clinical reporting involved selecting a vendor, integrating their product, and supporting a single proprietary language. The shift to report clinical trials using R has had a much broader impact than just swapping out a language, with it also catalysing the adoption of data science in statistical programming. For the team building the latest generation of SCEs, this has led to a complex eco-system of dynamic dependencies to enable reproducible research, the need to adapt to a much faster pace of development of the tools used, and facilitated bringing different elements of evidence generation like trial design, and real world evidence, to co-exist with statistical programming. During this talk, we’ll discuss this evolution, the underlying tensions we continue to tackle aspiring to balance innovation against business continuity, and the critical role SCE architecture plays facilitating a shift to data science.
James Black
Roche
The importance of the SCE in enabling our shift from proprietary programming to open-source data science
Historically building a great SCE for clinical reporting involved selecting a vendor, integrating their product, and supporting a single proprietary language. The shift to report clinical trials using R has had a much broader impact than just swapping out a language, with it also catalysing the adoption of data science in statistical programming. For the team building the latest generation of SCEs, this has led to a complex eco-system of dynamic dependencies to enable reproducible research, the need to adapt to a much faster pace of development of the tools used, and facilitated bringing different elements of evidence generation like trial design, and real world evidence, to co-exist with statistical programming. During this talk, we’ll discuss this evolution, the underlying tensions we continue to tackle aspiring to balance innovation against business continuity, and the critical role SCE architecture plays facilitating a shift to data science.