Inefficiency of randomization methods that balance on stratum margins and improvements with permuted blocks and a sequential method
Genentech, Inc, 1 DNA Way, South San Francisco, CA 94080, USA.Statistics in Medicine (Impact Factor: 1.83). 07/2012; 31(16):1699-706. DOI: 10.1002/sim.5345
Stratified permuted blocks randomization is commonly applied in clinical trials, but other randomization methods that attempt to balance treatment counts marginally for the stratification variables are able to accommodate more stratification variables. When the analysis stratifies on the cells formed by crossing the stratification variables, these other randomization methods yield treatment effect estimates with larger variance than does stratified permuted blocks. When it is truly necessary to balance the randomization on many stratification variables, it is shown how this inefficiency can be improved by using a sequential randomization method where the first level balances on the crossing of the strata used in the analysis and further stratification variables fall lower in the sequential hierarchy.
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ABSTRACT: Various methods exist in the literature for achieving marginal balance for baseline stratification variables in sequential clinical trials. One major limitation with balancing on the margins of the stratification variables is that there is an efficiency loss when the primary analysis is stratified. To preserve the efficiency of a stratified analysis one recently proposed approach balances on the crossing of the stratification variables included in the analysis, which achieves conditional balance for the variables. A hybrid approach to achieving both marginal and conditional balances in sequential clinical trials is proposed, which is applicable to both continuous and categorical stratification variables. Numerical results based on extensive simulation studies and a real dataset show that the proposed approach outperforms the existing ones and is particularly useful when both additive and stratified models are planned for a trial. Copyright © 2013 John Wiley & Sons, Ltd.Pharmaceutical Statistics 09/2013; 12(5). DOI:10.1002/pst.1587 · 0.83 Impact Factor
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ABSTRACT: Randomization is fundamental to the design and conduct of clinical trials. Simple randomization ensures independence among subject treatment assignments and prevents potential selection biases, yet it does not guarantee balance in covariate distributions across treatment groups. Ensuring balance in important prognostic covariates across treatment groups is desirable for many reasons. A broad class of randomization methods for achieving balance are reviewed in this paper; these include block randomization, stratified randomization, minimization, and dynamic hierarchical randomization. Practical considerations arising from experience with using the techniques are described. A review of randomization methods used in practice in recent randomized clinical trials is also provided. Copyright © 2015 Elsevier Inc. All rights reserved.Contemporary clinical trials 08/2015; DOI:10.1016/j.cct.2015.07.011 · 1.94 Impact Factor
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