Recent reports have called into question the reproducibility, validity
and translatability of the preclinical animal studies due to limitations
in their experimental design and statistical analysis. To this end, we
implemented a matching-based modelling approach for optimal intervention
group allocation, randomization and power calculations, which takes
full account of the complex animal characteristics at baseline prior to
interventions. In prostate cancer xenograft studies, the method
effectively normalized the confounding baseline variability, and
resulted in animal allocations which were supported by RNA-seq profiling
of the individual tumours. The matching information increased the
statistical power to detect true treatment effects at smaller sample
sizes in two castration-resistant prostate cancer models, thereby
leading to saving of both animal lives and research costs. The novel
modelling approach and its open-source and web-based software
implementations enable the researchers to conduct adequately-powered and
fully-blinded preclinical intervention studies, with the aim to
accelerate the discovery of new therapeutic interventions.