Outcome-Adaptive Allocation using Auxiliary and Primary Outcomes

Studies with delayed outcomes generally receive little benefit from adaptive allocation procedures. In this manuscript we present an optimal design for outcome-adaptive allocation by combining information from delayed primary outcomes and more quickly observed auxiliary outcomes. Bayesian methods are used to construct the joint distribution of these outcomes, which is used to estimate the components of the optimal allocation ratio. Simulation studies show this approach to be effective at achieving adaption even before the delayed outcome is observed.