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Activity Number: 575 - Translating Real World Data into Robust Evidence to Inform Decisions on Medical Product Development and Life Cycle Management
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #330338 Presentation
Title: Real World Data Analysis to Inform Clinical Trial Modeling and Simulation
Author(s): Zhaoling Meng* and Dimple Patel and Qi Tang and Nadia Gaudel-Dedieu and James Rogers
Companies: sanofi and sanofi and Sanofi and sanofi and Metrum Research Group
Keywords: modeling; clinical trial simulation ; real world data; prediction; assumption testing

Modeling and clinical trial simulation (CTS) has been increasingly utilized in drug development design and decision making to provide quantitative assessments and scenario testing, especially for complex situations. Cardiovascular (CV) safety outcome study is routinely required in diabetes drug approval with its well-recognized complexities. Recent empagliflozin approval for CV indication provides an additional risk reduction option for diabetes patients with high CV risks and, at the same time, presents a potential confounding in the CV effect assessment for future studies. Patients can either take empaglifozin concomitantly at baseline or during the blinded study phase, which impacts the CV effect assessment of the study drug. To ensure the CV outcome prediction accuracy and robustness, modeling and CTS is used to anticipate this impact and increase the study probability of success. In this exercise, explicit and informative assumptions are essential in addition to appropriately established modeling framework to mimic the future study. Real world data was used to estimate the concomitant CV effects with/without empagliflozin and inform the CTS.

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

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