Probability & Patients: The Stochastic Science Behind In Silico Trial Breakthroughs
Description
๐ช๐ต๐ฎ๐ ๐ถ๐ณ ๐๐ต๐ฒ ๐ป๐ฒ๐ ๐ ๐ฐ๐น๐ถ๐ป๐ถ๐ฐ๐ฎ๐น ๐๐ฟ๐ถ๐ฎ๐น ๐ฏ๐ฟ๐ฒ๐ฎ๐ธ๐๐ต๐ฟ๐ผ๐๐ด๐ต ๐ถ๐ ๐ฎ๐น๐ฟ๐ฒ๐ฎ๐ฑ๐ ๐ถ๐ป ๐๐ผ๐๐ฟ ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ? This episode explores the fascinating world of virtual patient models and stochastic engineering, unveiling how digital simulations are revolutionising healthcare clinical trials. Discover how simulated trials using sophisticated virtual patient modelsโcomplete with variations in age, body size, and disease progressionโare introducing groundbreaking innovations in patient care.
We delve into stochastic engineering models that cleverly integrate uncertainty into device design, offering more precise and realistic predictions of clinical outcomes. Learn about the power prior methodโa dynamic approach to combining digital and real-world evidence in clinical trials, enhancing efficiency and accelerating device delivery to patients whilst maintaining rigorous safety and accuracy standards.
Join us for an illuminating discussion at the cutting edge of healthcare innovation, where virtual modelling meets real-world patient impact.
ย
Haddad T, Himes A, Thompson L, Irony T, Nair R; MDIC Computer Modeling and Simulation Working Group Participants. Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. J Biopharm Stat. 2017;27(6):1089-1103.ย
United States
