Comparisons of minimization and Atkinson's algorithm

• Published in: Statistics in medicine 2010-03, Vol.29 (7-8), p.721-730
• Main Authors: Senn, Stephen, Anisimov, Vladimir V., Fedorov, Valerii V.
• Format: Article
• Language: English
• Subjects: Adrenal Cortex Hormones - adverse effects; Adrenal Cortex Hormones - therapeutic use; Algorithms; Asthma - drug therapy; Biostatistics - methods; Clinical trials; Computer Simulation - statistics & numerical data; covariates; dynamic allocation; Efficiency; Female; Humans; Male; Medical statistics; Medical treatment; Monte Carlo Method; Monte Carlo simulation; optimal design; Optimization algorithms; Patient Selection; randomization; Randomized Controlled Trials as Topic - statistics & numerical data; Statistical methods
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.3763

Some general points regarding efficiency in clinical trials are made. Reasons as to why fitting many covariates to adjust the estimate of the treatment effect may be less problematic than commonly supposed are given. Two methods of dynamic allocation of patients based on covariates, minimization and Atkinson's approach, are compared and contrasted for the particular case where all covariates are binary. The results of Monte Carlo simulations are also presented. It is concluded that in the cases considered, Atkinson's approach is slightly more efficient than minimization although the difference is unlikely to be very important in practice. Both are more efficient than simple randomization, although it is concluded that fitting covariates may make a more valuable and instructive contribution to inferences about treatment effects than only balancing them. Copyright © 2010 John Wiley & Sons, Ltd.