Dynamic Data Monitoring for On-Going Clinical Trials

• Activity Number: 131 - Topics in Clinical Trials
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Tai Xie* and Ping Gao and Peng Zhang and Yue Tu and Joe Shih
• Companies: Brightech International and Brightech International and Brightech International and Brightech International and Rutgers University
• Keywords: dynamic trial monitoring; adaptive design; machine leaning; artificial intelligence

Abstract

In light of FDA’s recent guidance on Adaptive Designs for Clinical Trials, we will discuss our recent development of dynamic data monitoring (DDM) technology for on-going clinical trials. Incorporated with EDC/IWRS, this technology utilizes machine learning and AI ideas to continuously monitor and optimize clinical trial designs and trial processes and to increase the chance of success for a promising drug or to terminate a “hopeless” drug earlier. This technology could be used for improving the current IDMC review process by allowing the committee to review the trend of data instead of a snapshot at each interim data lock. In addition, this technology can be used for implementing seamless phase 2/3 adaptive design, optimal dose selection, population selection, endpoint selection, Real World Evidence (RWE) monitoring, dynamic safety monitoring for pharmacovigilance and signal detection, etc. The proposed applications are backed by a series of well-established publications. We will share some simulation and case studies to demonstrate the technology.