Investigating Different Priors in Bayesian Continual Reassessment Method

Tuba Koc, Mehmet Ali Cengiz

Abstract


Clinical trials are studies that explore whether a treatment, drug or device is safe and effective for humans. These studies can show which medial method work best for certain diseases. A well-designed and a properly-analyzed clinical trial is a powerful tool for the development of new drugs. The first step in drug discovery (phase I) is very important to determine maximum tolerated dose (MTD).

In the first part of the study, the classical 3+3 design, Continual Reassessment Method (CRM), and Bayesian Continual Reassessment Method (B-CRM) are compared in terms of selection probability of MTD and the number of treated patients. Among these designs, the B-CRM produced better results than the 3+3 and the CRM. In the second part of the study, we considered different model structures and priors in the B-CRM design. We evaluated power model, hyperbolic tanget model and logit model for gamma, uniform and lognormal prior. We found that if power or hyperbolic tangent model structure and uniform prior was selected, the MTD selection rates was the highest in B-CRM.


Keywords


Clinical trial, maximum tolerated dose, Bayesian inference, prior selection

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