Randomization in Pre-Clinical Studies: When Evolution Theory Meets Statistics.

Abstract

Randomization is a statistical procedure used to allocate study subjects randomly into experimental groups while balancing continuous variables. This paper presents an alternative to random allocation for creating homogeneous groups by balancing experimental factors. The proposed algorithms, inspired by the Theory of Evolution, enhance the benefits of randomization through partitioning. The methodology employs a genetic algorithm that minimizes the Irini criterion to partition datasets into balanced subgroups. The algorithm's performance is evaluated through simulations and dataset examples, comparing it to random allocation via exhaustive search. Results indicate that the experimental groups created by Irini are more homogeneous than those generated by exhaustive search. Furthermore, the Irini algorithm is computationally more efficient, outperforming exhaustive search by more than three orders of magnitude.