A method for obtaining randomized block designs in preclinical studies with multiple quantitative blocking variables.
A method is proposed for block randomization of treatments to experimental units that can accommodate both multiple quantitative blocking variables and unbalanced designs. Hierarchical clustering in conjunction with leaf-order optimization is used to block experimental units in multivariate space. The method is illustrated in the context of a diabetic mouse assay. A simulation study is presented to explore the utility of the proposed randomization method relative to that of a completely randomized approach, both in the presence and absence of covariate adjustment. An example R function is provided to illustrate the implementation of the method.
Iturria
Stephen J
SJ
Global Statistical Sciences, Eli Lilly & Company, Indianapolis, IN, USA. stephen.j.iturria@monsanto.com
D000818:Animals; D003198:Computer Simulation; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D011897:Random Allocation