Mathematical programming tools for randomization purposes in small two‐arm clinical trials: A case study with real data

• Published in: Pharmaceutical statistics : the journal of the pharmaceutical industry Vol. 23; no. 6; pp. 794 - 812
• Main Authors: Vazquez, Alan R., Wong, Weng‐Kee
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.11.2024; Wiley Subscription Services, Inc
• Subjects: Adaptive Clinical Trials as Topic; Clinical trials; covariate‐adaptive trial; energy distance; Humans; Main Paper; Mathematical programming; minimization method; Models, Statistical; prior information; Random Allocation; Randomized Controlled Trials as Topic - methods; Randomized Controlled Trials as Topic - statistics & numerical data; Research Design - statistics & numerical data; Sample Size; minimization method; energy distance; prior information; covariate‐adaptive trial
• ISSN: 1539-1604; 1539-1612; 1539-1612
• DOI: 10.1002/pst.2388

Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate‐adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.