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Pre-Clinical SIG Webinar: Modern Algorithms for Animal Randomization in Preclinical Studies

18 November 2025

Randomizing subjects into treatment groups reduces confounding in clinical and preclinical studies, but simple complete randomization can fail in small trials (especially in vivo) where group imbalances are more likely. More structured methods (e.g., randomized block designs) help, but algorithmic allocation gives the best balance. One effective algorithmic approach uses a genetic-algorithm framework inspired by evolutionary theory: it searches for partitions of the dataset that minimize a predefined fitness function measuring imbalance. Compared with simple random allocation and exhaustive search, this genetic algorithm produced more homogeneous experimental groups, ran far faster than exhaustive search, and its computational advantage grows quickly with more subjects and more design factors. Extensions and related approaches (for example, simulated annealing) can further adapt or improve the method.

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