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All Times EDT

Thursday, September 22
Thu, Sep 22, 10:45 AM - 12:00 PM
Salon H
Novel Techniques in Leveraging Real-World Data: Beyond Propensity-Score Matching

Type I Error Control Strategies Using Conditional Hybrid Control in Randomized Clinical Trials (303712)

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Adam Hamm, Statistics and Data Corporation 
Hongyan Li, Statistics and Data Corporation 
*Qian Helen Li, Statistics and Data Corporation 

Keywords: External Trial Subjects, Hybrid Control, Randomized Clinical Trials, Type I Error

Leveraging external trial data in clinical trials has been a topic receiving increased attention. Of interest is the promising ethical benefit to reduce the number of subjects in control groups who may not receive treatment benefit and the promising operational benefit to design and conduct studies more efficiently. Recent research reveals issues with using hybrid controls by statistical matching in baseline characteristics, mainly because the contemporaneous and operational differences have not been considered between the external data and the trial data. Conditional borrowing approaches can substantially control both bias and type I error, but do have certain small type I error inflation in some circumstances. This presentation discusses several strategies that can improve the control of the type I error without great loss in power. Simulation results will be discussed and a case study on how best to apply the conditional hybrid approach will be discussed.