Reproducibility and Rigor in Animal-Based Research

Malcolm Macleod, Swapna Mohan

ILAR Journal · 2019 · 112 citations · 28 references

DOIFull text

Open access

Concepts

TL;DR

Reproducibility concerns in animal research have prompted a focused review of key issues in in vivo studies. The paper aims to define core concepts, examine epistemological implications, identify design and reporting weaknesses, and propose strategies to enhance reproducibility in animal-based research. The authors conduct a conceptual and evidence-based review, drawing on systematic reviews and experimental studies to analyze statistical significance, generalizability, and the value of failed replications. They conclude that methodological weaknesses hinder the translation of animal findings into human therapies and note the biomedical community’s growing, albeit uneven, response to these challenges.

Abstract

Abstract Increasing focus on issues of research reproducibility affords us the opportunity to review some of the key issues related in vivo research. First, we set out some key definitions, to guide the reader through the rest of the paper. Next we consider issues of epistemology, of how animal experiments lead to changes in our understanding of biomedicine and, potentially, to the development of new therapeutics. Here we consider the meaning of statistical significance; the importance of understanding whether findings have general truth; and the advances in knowledge which can result from ‘failed’ replication. Then, we consider weaknesses in the design, conduct and reporting of experiments, and review evidence for this from systematic reviews and from experimental studies addressing these issues. We consider the impact that these weaknesses have on the development of new treatments for human disease, and reflect on the response to these issues from the biomedical research community. Finally, we consider strategies for improvement including increased use of brief, pre-registered study protocols; pre-registration, open publication and open data; and the central importance of education in improving research performance.

References

28