alexa The method of minimization for allocation to clinical trials. a review.
Mathematics

Mathematics

Journal of Biometrics & Biostatistics

Author(s): Scott NW, McPherson GC, Ramsay CR, Campbell MK

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Abstract Minimization is a largely nonrandom method of treatment allocation for clinical trials. We conducted a systematic literature search to determine its advantages and disadvantages compared with other allocation methods. Minimization was originally proposed by Taves and by Pocock and Simon. The latter paper introduces a family of allocation methods of which Taves' method is the simplest example. Minimization aims to ensure treatment arms are balanced with respect to predefined patient factors as well as for the number of patients in each group. Further extensions of the method have also been proposed by other authors. Simulation studies show that minimization provides better balanced treatment groups when compared with restricted or unrestricted randomization and that it can incorporate more prognostic factors than stratified randomization methods such as permuted blocks within strata. Some more computationally complex methods may give an even better performance. Concerns over the use of minimization have centered on the fact that treatment assignments may be predicted with certainty in some situations and on the implications for the analysis methods used. It has been suggested that adjustment should always be made for minimization factors when analyzing trials where minimization is the allocation method used. The use of minimization may sometimes result in added organizational complexity compared with other methods. Minimization has been recommended by many commentators for use in clinical trials. Despite this it is still rarely used in practice. From the evidence presented in this review, we believe minimization to be a highly effective allocation method and recommend its wider adoption in the conduct of randomized controlled trials.
This article was published in Control Clin Trials and referenced in Journal of Biometrics & Biostatistics

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