Abstract
The goals of phase II clinical trials are to gain important information about the performance of novel treatments and decide whether to conduct a larger phase III trial. This can be complicated in cases when the phase II trial objective is to identify a novel treatment having several factors. Such multifactor treatment scenarios can be explored using fixed sample size trials. However, the alternative design could be response adaptive randomization with interim analyses and additionally, longitudinal modeling whereby more data could be used in the estimation process. This combined approach allows a quicker and more responsive adaptation to early estimates of later endpoints. Such alternative clinical trial designs are potentially more powerful, faster, and smaller than fixed randomized designs. Such designs are particularly challenging, however, because phase II trials tend to be smaller than subsequent confirmatory phase III trials. The phase II trial may need to explore a large number of treatment variations to ensure that the efficacy of optimal clinical conditions is not overlooked. Adaptive trial designs need to be carefully evaluated to understand how they will perform and to take full advantage of their potential benefits. This manuscript discusses a Bayesian response adaptive randomization design with a longitudinal model that uses a multifactor approach for predicting phase III study success via the phase II data. The approach is based on an actual clinical trial design for the hyperbaric oxygen brain injury treatment trial. Specific details of the thought process and the models informing the trial design are provided. Copyright © 2016 John Wiley & Sons, Ltd.