Early completion of phase I cancer clinical trials with Bayesian optimal interval design

Phase I cancer clinical trials have been proposed novel designs such as algorithm-based, model-based, and model-assisted designs. Model-based and model-assisted designs have a higher identification rate of maximum tolerated dose (MTD) than algorithm-based designs, but are limited by the fact that the sample size is fixed. Hence, it would be very attractive to estimate the MTD with sufficient accuracy and complete the trial early. O'Quigley proposed the early completion of a trial with the continual reassessment method among model-based designs when the MTD is estimated with sufficient accuracy. However, the proposed early completion method based on the binary outcome trees has a problem that the calculation cost is high when the number of remaining patients is large. Among model-assisted designs, the Bayesian optimal interval (BOIN) design provides the simplest approach for dose adjustment. We propose the novel early completion method for the clinical trials with the BOIN design when the MTD is estimated with sufficient accuracy. This completion method can be easily calculated. In addition, the method does not require many more patients treated for the determination of early completion. We confirm that the BOIN design applying the early completion method has almost the same MTD identification rate compared to the BOIN design through simulations conducted based on over 30 000 scenarios.