4.0 Article

A reaction norm perspective on reproducibility

期刊

THEORY IN BIOSCIENCES
卷 140, 期 2, 页码 169-176

出版社

SPRINGER
DOI: 10.1007/s12064-021-00340-y

关键词

Reproducibility; Norm of reaction; Random lab model; Between-laboratory variation; In-vivo research

资金

  1. Swiss National Science Foundation, SNF Grant [310030-179254]
  2. Swiss National Science Foundation (SNF) [310030_179254] Funding Source: Swiss National Science Foundation (SNF)

向作者/读者索取更多资源

Reproducibility in biomedical research, particularly in preclinical animal research, has been seriously questioned due to various threats such as lack of scientific rigour, low statistical power, publication bias, analytical flexibility, and fraud. The lack of external validity caused by rigorous standardization of both animals and environment may also contribute to the reproducibility crisis. A reaction norm approach acknowledging gene-by-environment interactions offers a new perspective on how dominating environmental effects can impact studies and effect size estimates.
Reproducibility in biomedical research, and more specifically in preclinical animal research, has been seriously questioned. Several cases of spectacular failures to replicate findings published in the primary scientific literature have led to a perceived reproducibility crisis. Diverse threats to reproducibility have been proposed, including lack of scientific rigour, low statistical power, publication bias, analytical flexibility and fraud. An important aspect that is generally overlooked is the lack of external validity caused by rigorous standardization of both the animals and the environment. Here, we argue that a reaction norm approach to phenotypic variation, acknowledging gene-by-environment interactions, can help us seeing reproducibility of animal experiments in a new light. We illustrate how dominating environmental effects can affect inference and effect size estimates of studies and how elimination of dominant factors through standardization affects the nature of the expected phenotype variation through the reaction norms of small effect. Finally, we discuss the consequences of reaction norms of small effect for statistical analysis, specifically for random effect latent variable models and the random lab model.

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