title: Bayesian response adaptive randomization using longitudinal outcomes.
abstract: The response adaptive randomization (RAR) method is used to increase the number of patients assigned to more efficacious treatment arms in clinical trials. In many trials evaluating longitudinal patient 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 longitudinal patient outcomes (longitudinal RAR). We use a Bayesian linear mixed effects model to analyze longitudinal continuous patient outcomes for calculating a patient allocation probability. In addition, we aim to mitigate the loss of statistical power because of large patient allocation imbalances by embedding adjusters into the patient allocation probability calculation. Using extensive simulation we compared the operating characteristics of our proposed longitudinal 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 longitudinal RAR method assigned more patients to the presumably superior treatment arm compared with the other two methods. In addition, the embedded adjuster effectively worked to prevent extreme patient 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 longitudinal data model.
authors.affiliation: Department of Biostatistics and Epidemiology, Graduate School of Medicine, Yokohama City University, Yokohama, Japan.|Department of Biostatistics, A2 Healthcare Corporation, Tokyo, Japan.
authors.forename: Tomoyoshi
authors.initials: T
authors.lastname: Hatayama
authors.affiliation: Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Morita
authors.affiliation: Department of Biostatistics and Epidemiology, Graduate School of Medicine, Yokohama City University, Yokohama, Japan.
authors.forename: Kentaro
authors.initials: K
authors.lastname: Sakamaki
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D011336:Probability; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016896:Treatment Outcome
keywords: Bayesian adaptive design; longitudinal outcomes; predictive power; response adaptive randomization