A global pharmaceutical leader’s Oncology clinical trial department faced major challenges in transforming and standardizing clinical trial SAS datasets after acquiring another company. The incoming clinical trial SAS datasets needed to be accessed within the R environment (Posit Workbench) to create Analysis Ready Datasets (ARD).

Each dataset had to assemble a specific set of attributes from different domain datasets, which were being read and parsed in the R Posit environment. The structural inconsistencies within the acquired company’s domain datasets made it difficult to establish a unified approach for data merging and transformation. This variability, combined with the need to manually intervene across different study setups, led to a process that was labor-intensive, error-prone, and hard to scale.  Also, some of the Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) datasets were a decade old and didn’t comply with Clinical Data Interchange Standards Consortium (CDISC) guidelines.

To meet internal standards, this client wanted the data to be restructured into PK/PD Analysis-Ready Data Sets (ARD). This process required complex data mapping, generation of custom attributes, and strict adherence to in-house standard operating practices.

Amid all concerns over timelines, quality, and compliance, there was a critical need for a scalable, automated solution to handle study variability and ensure data integrity.