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

Alan R. Vazquez, Weng Kee Wong

研究成果: Article同行評審

摘要

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.

原文English
期刊Pharmaceutical Statistics
DOIs
出版狀態Accepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 藥理
  • 藥學(醫學)

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