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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 #328856 Presentation
Title: Evaluating Different Analytic Strategies to Translate Real World Data to Robust Evidence for Decision Making
Author(s): Hongwei Wang* and Weili He and Yabing Mai and Meijing Wu and Dajun Tian
Companies: AbbVie Inc and AbbVie and AbbVie, Inc and AbbVie and Chiltern
Keywords: real world data; confounding; propensity score; marginal model; conditional model

While well designed randomized clinical trials remain the gold standard to establish efficacy and safety profile of a medical intervention, their generalizability to a wider population and lack of head to head comparison with relevant treatment options warrants further research. Real world evidence, especially that of comparative effectiveness, plays a critical role in filling this gap. As no randomization is involved, selection bias leads to imbalance of key patients' characteristics across different treatment groups. Various analytic frameworks have been proposed to control for this inherent confounding, which include conditional model of multivariate regression and marginal model of propensity score based method. The performance of these methods in terms of bias reduction, coverage of confidence interval, power to detect a true difference, and Type I error control require evaluation to understand the performance characteristics. In this talk, we will present simulation study results evaluating the performance characteristics of a few commonly used methods. Recommendations for their practical usage will be given.

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

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