Improving Statistical Rigor in Animal Aging Research by Addressing Clustering and Nesting Effects: Illustration with the National Institute on Aging’s Intervention Testing Program Data

Clustering effects, such as those introduced by housing animals in shared cages, are often overlooked in preclinical lifespan studies, despite their potential to underestimate variance estimates and inflate Type I error rates, leading to misleading conclusions. This methodological oversight reduces statistical rigor and may undermine the reliability of findings. To address this gap, the current study examines the impact of accounting for a clustering and nesting effect on lifespan analyses by comparing the results of statistical models which both account for and ignore this effect. Using 2019 data from the Interventions Testing Program (ITP), a large-scale initiative evaluating the effects of compounds on lifespan in UM-HET3 mice as a case study, we illustrate how different modeling approaches influence statistical estimates and conclusions. The clustering and nesting effect was addressed using linear mixed-effects and Cox frailty models, both of which explicitly account for cage-level dependencies and the nesting of cages within treatment. Comparisons were made between unadjusted lifespan analyses and those incorporating the clustering and nesting adjustment. The results of this case study indicate that properly adjusting for a clustering and nesting effect can change the conclusions drawn from statistical significance tests as compared to unadjusted model approaches, and so it remains best practice to properly account for clustering and nesting to reduce the potential for inflated Type I error rates. These findings highlight the importance of accounting for clustering and nesting in preclinical research to ensure valid and robust statistical inference.