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Statistical properties of minimal sufficient balance and minimization as methods for controlling baseline covariate imbalance at the design stage of sequential clinical trials
Author(s) -
Lauzon Steven D.,
Ramakrishnan Viswanathan,
Nietert Paul J.,
Ciolino Jody D.,
Hill Michael D.,
Zhao Wenle
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8552
Subject(s) - covariate , randomness , minification , baseline (sea) , computer science , statistics , type i and type ii errors , balance (ability) , mathematical optimization , mathematics , econometrics , medicine , oceanography , physical medicine and rehabilitation , geology
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.