Statistical properties of minimal sufficient balance and minimization as methods for controlling baseline covariate imbalance at the design stage of sequential clinical trials

Steven D. Lauzon*, Viswanathan Ramakrishnan, Paul J. Nietert, Jody D. Ciolino, Michael D. Hill, Wenle Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

When the number of baseline covariates whose imbalance needs to be controlled in a sequential randomized controlled trial is large, minimization is the most commonly used method for randomizing treatment assignments. The lack of allocation randomness associated with the minimization method has been the source of controversy, and the need to reduce even minor imbalances inherent in the minimization method has been challenged. The minimal sufficient balance (MSB) method is an alternative to the minimization method. It prevents serious imbalance from a large number of covariates while maintaining a high level of allocation randomness. In this study, the two treatment allocation methods are compared with regards to the effectiveness of balancing covariates across treatment arms and allocation randomness in equal allocation clinical trials. The MSB method proves to be equal or superior in both respects. In addition, type I error rate is preserved in analyses for both balancing methods, when using a binary endpoint.

Original languageEnglish (US)
Pages (from-to)2506-2517
Number of pages12
JournalStatistics in Medicine
Volume39
Issue number19
DOIs
StatePublished - Aug 30 2020

Keywords

  • allocation randomness
  • baseline covariate imbalance
  • clinical trial
  • minimal sufficient balance
  • minimization

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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