title: Quantitative comparison of randomization designs in sequential clinical trials based on treatment balance and allocation randomness.
abstract: To evaluate the performance of randomization designs under various parameter settings and trial sample sizes, and identify optimal designs with respect to both treatment imbalance and allocation randomness, we evaluate 260 design scenarios from 14 randomization designs under 15 sample sizes range from 10 to 300, using three measures for imbalance and three measures for randomness. The maximum absolute imbalance and the correct guess (CG) probability are selected to assess the trade-off performance of each randomization design. As measured by the maximum absolute imbalance and the CG probability, we found that performances of the 14 randomization designs are located in a closed region with the upper boundary (worst case) given by Efron's biased coin design (BCD) and the lower boundary (best case) from the Soares and Wu's big stick design (BSD). Designs close to the lower boundary provide a smaller imbalance and a higher randomness than designs close to the upper boundary. Our research suggested that optimization of randomization design is possible based on quantified evaluation of imbalance and randomness. Based on the maximum imbalance and CG probability, the BSD, Chen's biased coin design with imbalance tolerance method, and Chen's Ehrenfest urn design perform better than popularly used permuted block design, EBCD, and Wei's urn design.
authors.affiliation: Department of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA. zhaow@musc.edu
authors.forename: Wenle
authors.initials: W
authors.lastname: Zhao
authors.forename: Yanqiu
authors.initials: Y
authors.lastname: Weng
authors.forename: Qi
authors.initials: Q
authors.lastname: Wu
authors.forename: Yuko
authors.initials: Y
authors.lastname: Palesch
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D011336:Probability; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design; D015983:Selection Bias