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Activity Number: 273 - How Advanced Analytic Tools Deliver Insights for Clinical Investigations Through Real World Data
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #300004
Title: Predict Phase 3 Clinical Trial Results Using Phase 2 Data and Electronic Health Records
Author(s): Qi Tang* and Youran Qi
Companies: Sanofi and University of Wisconsin
Keywords: predict Phase 3; heterogeneity; electronic health records; real world data; deep learning

Predicting Phase 3 clinical trial results is a critical step of Go/No-Go decision making and optimization of Phase 3 clinical trial designs. Traditionally, because of limited historical efficacy and safety data about the investigational product, a homogeneity assumption is often made in such predictions that Phase 3 patients are similar to Phase 2 patients in terms of distributions of baseline characteristics. However, Phase 3 clinical trials are larger than Phase 2 clinical trials and such homogeneity assumption is often violated. Thanks to availability of electronic health records, we can better simulate the Phase 3 patients with a random sample of the real world data. Thus, we propose a deep learning approach, where the Phase 2 clinical trial data and the real world data are coupled together to predict Phase 3 clinical trial results without the above homogeneity assumption. We demonstrated that the proposed method can yield much more accurate and robust predictions.

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

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