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Activity Number: 399 - Recent Developments in Precision Medicine
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Biopharmaceutical Section
Abstract #312837
Title: Personalized Monitoring Is Good for Patients and Clinical Science
Author(s): Glen Wright Colopy*
Companies: Cenduit LLC
Keywords: Precision medicine; Clinical trial; personalized medicine; machine learning; patient specific; risk based monitoring

Patient data from clinical trials exhibit dynamics both (i) from top-down rules of the intended protocol and (ii) natural variability in patients, time, and clinical settings. Rules-based algorithmic monitoring of clinical trial data provides a foundation for detecting anomalous deviations from the trial protocol, but suffers because sensitivity and specificity of detection is heavily dependent on a priori definitions of the monitoring rules. Statistical monitoring allows greater flexibility by replacing dichotomized thresholds with continuities over distributions. Furthermore, probabilistic inference provides a principled manner in which to reason when conditioning data distributions, ex. with respect to a specific patient, time, or covariate. We illustrate how patient-specific data can enrich rules-based systems by ensuring the rules are built with greater clinical personalized time-dependent context. Next we demonstrate how building patient-specific models can identify poor quality data that would be missed by population-based statistics or rule-based methods. These personalized models can be visualized and interpreted by nontechnical experts monitoring the trial.

Authors who are presenting talks have a * after their name.

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