In pharmaceutical product development, statistical programming and the analysis of clinical trial data has always played a key role in helping to deliver medicines to patients. Over the last decade, the industry landscape and analysis needs have changed dramatically. Most organizations have shifted from a one-size-fits-all approach to personalized healthcare, where genetics and other biological information influence individual treatment. Clinical trial design and analysis has become more innovative—and complex—with the use of synthetic control arms, patient reported outcomes, real-world evidence, and biomarker data. Industry data standards under CDISC have created guidance for most data sources but novel endpoints might require additional considerations. Statistical programmers and analysts need to ensure that data is FAIR—findable, accessible, interoperable, and reusable; this is critical to the longevity of an organization who prioritizes their data as an asset. SAS continues to lead the industry in data analysis but its use has also evolved to where it now can be used with other programming languages, contributing to interactive visualizations and automation. This influences how and what we use to analyze data. This presentation provides an overview of statistical programming and analysis in the pharmaceutical industry, including how skills and responsibilities have adapted and advanced the impact of bringing medicines to patients sooner
Description:
In pharmaceutical product development, statistical programming and the analysis of clinical trial data has always played a key role in helping to deliver medicines to patients. Over the last decade, the industry landscape and analysis needs have changed dramatically. Most organizations have shifted from a one-size-fits-all approach to personalized healthcare, where genetics and other biological information influence individual treatment. Clinical trial design and analysis has become more innovative—and complex—with the use of synthetic control arms, patient reported outcomes, real-world evidence, and biomarker data. Industry data standards under CDISC have created guidance for most data sources but novel endpoints might require additional considerations. Statistical programmers and analysts need to ensure that data is FAIR—findable, accessible, interoperable, and reusable; this is critical to the longevity of an organization who prioritizes their data as an asset. SAS continues to lead the industry in data analysis but its use has also evolved to where it now can be used with other programming languages, contributing to interactive visualizations and automation. This influences how and what we use to analyze data. This presentation provides an overview of statistical programming and analysis in the pharmaceutical industry, including how skills and responsibilities have adapted and advanced the impact of bringing medicines to patients sooner