title: Simulation study for evaluating the performance of response-adaptive randomization.
abstract: A response-adaptive randomization (RAR) design refers to the method in which the probability of treatment assignment changes according to how well the treatments are performing in the trial. Holding the promise of treating more patients with the better treatments, RARs have been successfully implemented in clinical trials. We compared equal randomization (ER) with three RARs: Bayesian adaptive randomization, sequential maximum likelihood, and sequential posterior mean. We fixed the total number of patients, considered as patient horizon, but varied the number of patients in the trial. Among the designs, we compared the proportion of patients assigned to the superior arm, overall response rate, statistical power, and total patients enrolled in the trial with and without adding an efficacy early stopping rule. Without early stopping, ER is preferred when the number of patients beyond the trial is much larger than the number of patients in the trial. RAR is favored for large treatment difference or when the number of patients beyond the trial is small. With early stopping, the difference between these two types of designs was reduced. By carefully choosing the design parameters, both RAR and ER methods can achieve the desirable statistical properties. Within three RAR methods, we recommend SPM considering the larger proportion in the better arm and higher overall response rate than BAR and similar power and trial size with ER. The ultimate choice of RAR or ER methods depends on the investigator's preference, the trade-off between group ethics and individual ethics, and logistic considerations in the trial conduct, etc.
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA; Division of Biostatistics, School of Public Health, The University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA.
authors.forename: Yining
authors.initials: Y
authors.lastname: Du
authors.affiliation: Baylor Institute for Immunology Research, Baylor Research Institute, Baylor Health Care System, Dallas, TX 75246, USA.
authors.forename: Xuan
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA. Electronic address: jjlee@mdanderson.org.
authors.forename: J
authors.initials: J
authors.lastname: Jack Lee
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design
keywords: Allocation probability; Bayesian adaptive design; Efficacy early stopping; Operating characteristics; Patient horizon