title: Two-way minimization: a novel treatment allocation method for small trials.
abstract: Randomization is a hallmark of clinical trials. If a trial entails very few subjects and has many prognostic factors (or many factor levels) to be balanced, minimization is a more efficient method to achieve balance than a simple randomization. We propose a novel minimization method, the 'two-way minimization'. The method separately calculates the 'imbalance in the total numbers of subjects' and the 'imbalance in the distributions of prognostic factors'. And then to allocate a subject, it chooses--by probability--to minimize either one of these two aspects of imbalances. As such, it is a method that is both treatment-adaptive and covariate-adaptive. We perform Monte-Carlo simulations to examine its statistical properties. The two-way minimization (with proper regression adjustment of the force-balanced prognostic factors) has the correct type I error rates. It also produces point estimates that are unbiased and variance estimates that are accurate. When there are important prognostic factors to be balanced in the study, the method achieves the highest power and the smallest variance among randomization methods that are resistant to selection bias. The allocation can be done in real time and the subsequent data analysis is straightforward. The two-way minimization is recommended to balance prognostic factors in small trials.
authors.affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Lan-Hsin
authors.initials: LH
authors.lastname: Chen
authors.forename: Wen-Chung
authors.initials: WC
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011379:Prognosis; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic; D015203:Reproducibility of Results; D012107:Research Design; D015983:Selection Bias; D012680:Sensitivity and Specificity