In Silico Trials, Real Impacts!

Probability & Patients: The Stochastic Science Behind In Silico Trial Breakthroughs


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๐—ช๐—ต๐—ฎ๐˜ ๐—ถ๐—ณ ๐˜๐—ต๐—ฒ ๐—ป๐—ฒ๐˜…๐˜ ๐—ฐ๐—น๐—ถ๐—ป๐—ถ๐—ฐ๐—ฎ๐—น ๐˜๐—ฟ๐—ถ๐—ฎ๐—น ๐—ฏ๐—ฟ๐—ฒ๐—ฎ๐—ธ๐˜๐—ต๐—ฟ๐—ผ๐˜‚๐—ด๐—ต ๐—ถ๐˜€ ๐—ฎ๐—น๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—ถ๐—ป ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฐ๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ฒ๐—ฟ? 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 modelsโ€”complete with variations in age, body size, and disease progressionโ€”are 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 methodโ€”a 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 

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In Silico Trials, Real Impacts!By UK CEiRSI | InSilicoUK