Monitor On-Going Clinical Trials with a Dynamic Procedure
Dr. Joe Shih, Professor at Rutgers, Sr. Advisor of CIMS
Tai Xie, Ph.D., CEO of CIMS Global
|Webinar Date & Time||Speaker||Title||Length||Bio/Abstract|
|6/26/2020 10:30AM -12:00PM||Dr. Tai Xie||Monitor On-Going Clinical Trials with a Dynamic Procedure||45 mins||Received|
|Dr. Joe Shih||DDM system application in the recent Chinese randomized double-blind clinical trial of remdesivir in treating severe COVID-19 patients||45 mins|
According to a recent report, nearly 70% Phase II trials failed to move forward to Phase III. Part of the reasons caused such high failure rate could be inefficient trial design and trial monitoring. In this talk, we introduce the concept of dynamic adaptive design (DAD) and dynamic data monitoring (DDM). We develop the principles and procedures for dynamically monitoring on-going clinical trials and demonstrate that the accumulative treatment effect can be automatically estimated and continuously accessible over the information time. By taking the advantage of e-clinical and A.I. technologies, we propose the framework for constructing a clinical trial “radar” system on which the Wald statistics, conditional power, trend, timing for sample size re-estimation, alerting of early stopping for efficacy and/or futility can be automatically displayed and the trail can be intelligently monitored. We demonstrate the application of DDM in IDMC practice and provide examples of DDM as a diagnosis tool for two real studies to retrospectively understand what was going on as data being accumulated. We also demonstrate that the proposed procedure could help to identify an optimal design and improve the trial efficiency in the sense of saving “hopeful” trials or terminating “hopeless” trials. Simulation results show that the type I error rate is well controlled in the DDM procedure.