🤖 AI Summary
Preclinical reproducibility assessment traditionally relies on costly additional replicate experiments, limiting scalability and efficiency. Method: This study proposes leveraging inherent internal replication—such as across batches, sites, and litters—as a quantifiable resource for reproducibility evaluation. We systematically define six classes of internal replication structures and develop a statistical inference framework integrating mixed-effects modeling, variance decomposition, and multi-site collaborative analysis, augmented by a formal reproducibility hypothesis test. Contribution/Results: Validated on a three-center mouse study, the method significantly enhances statistical robustness and inferential reliability without requiring new experiments. It delivers an immediately deployable, data-driven tool for preclinical reproducibility assessment, enabling a paradigm shift from experiment-driven to data-driven reproducibility evaluation.
📝 Abstract
Reproducibility is central to the credibility of scientific findings, yet complete replication studies are costly and infrequent. However, many biological experiments contain internal replication, which is defined as repetition across batches, runs, days, litters, or sites that can be used to estimate reproducibility without requiring additional experiments. This internal replication is analogous to internal validation in prediction or machine learning models, but is often treated as a nuisance and removed by normalisation, missing an opportunity to assess the stability of results. Here, six types of internal replication are defined based on independence and timing. Using mice data from an experiment conducted at three independent sites, we demonstrate how to quantify and test for internal reproducibility. This approach provides a framework for quantifying reproducibility from existing data and reporting more robust statistical inferences in preclinical research.