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Comparisons of minimization and Atkinson's algorithm
Author(s) -
Senn Stephen,
Anisimov Vladimir V.,
Fedorov Valerii V.
Publication year - 2010
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.3763
Subject(s) - covariate , minification , monte carlo method , computer science , simple (philosophy) , randomization , algorithm , binary number , econometrics , statistics , mathematical optimization , mathematics , clinical trial , machine learning , medicine , pathology , philosophy , epistemology , arithmetic
Abstract 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.