Finding Near-Optimal Bayesian Experimental Designs via Genetic Algorithms

Whole-cage randomization for animal studies with unequal cage or group sizes

Following good statistical practice, in vivo study investigators allocate animals into two or more treatment groups using a randomization routine to eliminate selection bias and balance known and unknown confounding factors. For some studies, however, randomization at the individual animal level cannot be implemented. For example, for studies that involve co-housed male mice, an animal-level randomization can place unfamiliar mice together in the same cage, which can trigger fighting. To meet the ethical obligations to enhance the welfare of an animal used in science, the experimental procedures are, therefore, often modified, and male mice, possibly from the same brood, may be housed together. It follows that animal allocation into groups must proceed at the whole-cage level. Given the small sample sizes in animal studies, controlling baseline variables can be quite challenging. The difficulty greatly increases with a whole-cage randomization restriction. When the number of animals per cage or the treatment group sizes are unequal, there is no algorithm in the literature to perform the task. We propose a novel, fast, and reliable algorithm to provide a whole-cage randomization that balances one or more baseline variables across groups. The algorithm was applied to a realistic example dataset.