Our healthcare client was conducting a Phase III clinical trial to evaluate the efficacy of a new antihypertensive drug. Traditionally, they relied on SAS for data analysis, but the team decided to integrate R programming to increase flexibility, transparency, and reproducibility.
The team streamlined data cleaning and transformation processes, automated the generation of Tables, Listings, and Figures (TLFs), and improved reproducibility and transparency in statistical analyses using version-controlled R scripts.
The R-based workflow reduced analysis time by 30% compared to the SAS-only approach, enhanced reproducibility, and improved collaboration between statisticians and data scientists.
Our healthcare client was conducting a Phase III clinical trial to evaluate the efficacy of a new antihypertensive drug. Traditionally, they relied on SAS for data analysis, but the team decided to integrate R programming to increase flexibility, transparency, and reproducibility.
The team streamlined data cleaning and transformation processes, automated the generation of Tables, Listings, and Figures (TLFs), and improved reproducibility and transparency in statistical analyses using version-controlled R scripts.
The R-based workflow reduced analysis time by 30% compared to the SAS-only approach, enhanced reproducibility, and improved collaboration between statisticians and data scientists.
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