Inefficiency of randomization methods that balance on stratum margins and improvements with permuted blocks and a sequential method
ABSTRACT 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 · 1.10 Impact Factor