We propose and evaluate a two-stage, phase 2, adaptive clinical trial design. Its goal is to determine whether future phase 3 (confirmatory) trials should be conducted, and if so, which population should be enrolled. The population selected for phase 3 enrollment is defined in terms of a disease severity score measured at baseline. We optimize the phase 2 trial design and analysis in a decision theory framework. Our utility function represents a combination of the cost of conducting phase 3 trials and, if the phase 3 trials are successful, the improved health of the future population minus the cost of treatment. Given such a utility function and a discrete prior distribution on the conditional treatment effect, we compute the Bayes optimal adaptive design. The resulting design is compared to simpler designs in simulation studies. We also apply the designs to resampled data from a completed, phase 2 trial evaluating a new surgical intervention for stroke.


Biostatistics | Statistical Methodology

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