Historical Approaches to Statistical Computing

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Historical Approaches to Statistical Computing

For organizations that do not yet have a statistical computing environment, managing statistical analyses is a manual, disjointed, and highly regimented SOP-driven process for clinical programmers, statistical programmers, data scientists and statisticians. Historically, electronic content-driven approaches for statistical analysis relied upon a dedicated server folder structure, controlled by a network administrator. These approaches require manual sign-off, validation of programs in disparate systems, and often resulted in difficult, time-consuming audits and cumbersome rerun processes.

The first SCEs were able to remove part of the administrative burden. Access control, validation status and version management were very helpful, although advanced features such as programming run order, control of data source changes, and linkage to metadata sources and change history were not yet available in these systems.

In more recent years, large sponsors and CROs have implemented customized SAS-based tools to solve the challenges associated with statistical analysis. While these bespoke solutions can improve analysis and programming workflows, they have been heavily process-driven with long implementation timelines and do not have the flexibility to support additional programming languages — such as R & Python — that are continuing to gain traction among data scientists and statisticians.