Improving Statistical Rigor in Animal Aging Research by Addressing Clustering and Nesting Effects: Illustration with...

Clustering effects, such as those introduced by housing animals in shared cages, are often overlooked in preclinical lifespan studies, despite their potential to distort 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 clustering and nesting effects on lifespan analyses by comparing the results of statistical models which both account for and ignore these effects. 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. Clustering and nesting effects were addressed using linear mixed effects, and Cox frailty models, both of which explicitly account for cage-level dependencies and different levels of data nesting. Comparisons were made between unadjusted lifespan analyses and those incorporating clustering and nesting adjustments. The results of this case study indicate that properly adjusting for clustering and nesting effects 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. By demonstrating the practical application of clustering adjustments, this work underscores the broader implications for improving reproducibility and rigor in lifespan studies and other experimental designs.