Bayesian response adaptive randomization using XXXX outcomes.

@entity1 @entity3057
UNASSIGNED: The response adaptive randomization (RAR) method is used to increase the number of @entity1 assigned to more efficacious treatment arms in clinical trials. In many trials evaluating @entity3057 @entity1 outcomes, RAR methods based only on the final measurement may not benefit significantly from RAR because of its delayed initiation. We propose a Bayesian RAR method to improve RAR performance by accounting for @entity3057 @entity1 outcomes ( @entity3057 RAR). We use a Bayesian linear mixed effects model to analyze @entity3057 continuous @entity1 outcomes for calculating a @entity1 allocation probability. In addition, we aim to mitigate the loss of statistical power because of large @entity1 allocation imbalances by embedding adjusters into the @entity1 allocation probability calculation. Using extensive simulation we compared the operating characteristics of our proposed @entity3057 RAR method with those of the RAR method based only on the final measurement and with an equal randomization method. Simulation results showed that our proposed @entity3057 RAR method assigned more @entity1 to the presumably superior treatment arm compared with the other two methods. In addition, the embedded adjuster effectively worked to prevent extreme @entity1 allocation imbalances. However, our proposed method may not function adequately when the treatment effect difference is moderate or less, and still needs to be modified to deal with unexpectedly large departures from the presumed @entity3057 data model. Copyright    2015 John Wiley _ Sons, Ltd.
