The randomization process is pivotal forclinical trials with more than one treatmentarm. This is because the same boundary con-ditions have to be used whenever new thera-pies are being compared with standard treat-ments or other experimental treatments.Kendall [3] describes “Chance” as a randomerror appearing to cause an association be-tween an intervention and an outcome. Themost important design strategy to minimizesuch random error is to have a large samplesize. Randomization is based on four mathe-maticalconcepts: (i) completerandomizationwith probability p = 1/n for the choice of onetreatmentout of n with identically distributedBernoulli random variables, (ii) restrictedrandomization with a variance-covariancematrix unequal 1/4 I (unique matrix), (iii)covariate adapted, and (iv) response adaptedrandomization. The random process itself isdrivenbyapseudo-randomnumbergeneratorusing, e.g. the linear congruential generatorX
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Schrimpf et al. (2010) studied this question.
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