Clinical Data Science And Statistical Programming

As the realms of clinical research expand, demonstrating the safety and efficacy of a medical product through the confines of a clinical trial protocol is not enough. Regulators are requiring drug developers to analyze data through a broader lens that accounts for biodiversity, real-world use, and targeted genomic, cultural, and environmental factors that could influence medical outcomes. The scientific discipline of translational medicine, which takes a “bench-to-bedside” approach to clinical research is quickly becoming the gold standard in the industry.

ThoughtSphere’s integrated Modeling and Analysis Programming (MAP) module allows data scientists and biostatisticians to seamlessly develop data models using SAS, R, and Python, with no data transfers or exports required. Models can be developed using real-time data curated in ClinHUB, our patented data ingestion engine, as well as imported external historical and RWE data sets. Using MAP data scientists can create dynamic AI/ML models to predict outcomes and find patterns in complex, unstructured data. Statistical models can also be created to test hypotheses, set benchmarks, and monitor data throughout the trial.