Journal
STATISTICS IN MEDICINE
Volume 31, Issue 29, Pages 3858-3873Publisher
WILEY-BLACKWELL
DOI: 10.1002/sim.5448
Keywords
linear model; unit-treatment additivity; permuted-blocks randomization; biased-coin randomization; minimization; randomization test
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Randomization models are useful in supporting the validity of linear model analyses applied to data from a clinical trial that employed randomization via permuted blocks. Here, a randomization model for clinical trials data with arbitrary randomization methodology is developed, with treatment effect estimators and standard error estimators valid from a randomization perspective. A central limit theorem for the treatment effect estimator is also derived. As with permuted-blocks randomization, a typical linear model analysis provides results similar to the randomization model results when, roughly, unit effects display no pattern over time. A key requirement for the randomization inference is that the unconditional probability that any patient receives active treatment is constant across patients; when this probability condition is violated, the treatment effect estimator is biased from a randomization perspective. Most randomization methods for balanced, 1 to 1, treatment allocation satisfy this condition. However, many dynamic randomization methods for planned unbalanced treatment allocation, like 2 to 1, do not satisfy this constant probability condition, and these methods should be avoided. Copyright (C) 2012 John Wiley & Sons, Ltd.
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