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Minimization: A new method of assigning patients to treatment and control groups
Author(s) -
Taves Donald R.
Publication year - 1974
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt1974155443
Subject(s) - randomization , minification , random variate , treatment and control groups , reduction (mathematics) , control (management) , statistics , clinical trial , computer science , control variates , blocking (statistics) , restricted randomization , mathematics , medicine , mathematical optimization , artificial intelligence , monte carlo method , random variable , hybrid monte carlo , geometry , markov chain monte carlo
This paper describes a new method of assigning patients to treatment and control groups to minimize differences between the groups, not only in the number of patients but in patient characteristics. Testing the method by computer simulations, using data on 40 patients with 15 variates each, demonstrates a four‐ to fivefold reduction of the probability of severe imbalance, relative to randomization. Minimization can maintain tight control of one variate, comparable to the currently acceptable experimental design of blocking, while reduCing the probability of severe imbalance in the other 14 variates by a factor of 3. It also compares favorably with accepted methods regarding susceptibility to experimenter bias. Therefore, it is suggested that minimization should replace randomization in assigning patients in clinical trials.