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Statistical practice and transparent reporting in the neurosciences: Preclinical motor behavioral experiments
Olivia Hogue1,2, Tucker Harvey3, Dena Crozier4
1Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.
Plos One
|March 21, 2022
Summary
Many preclinical animal studies analyzing complex behavioral data use flawed statistical methods. This impacts research reproducibility and translational decision-making, highlighting a need for improved statistical practices in neuroscience.
Area of Science:
- Neuroscience
- Translational Research
- Animal Models
Background:
- Longitudinal and behavioral preclinical animal studies generate complex data.
- Statistical analyses in this field often do not adequately account for data complexity.
- This can lead to overly optimistic conclusions, affecting research reproducibility and translational decision-making.
Purpose of the Study:
- To rigorously review statistical choices and reporting in published preclinical animal studies.
- To evaluate statistical shortcomings in the analysis of complex longitudinal and behavioral data.
- To identify common statistical errors in motor rehabilitation research for neurologic conditions.
Main Methods:
- Cross-sectional meta-research study of controlled mouse or rat experiments.
- Searched Medline via PubMed for English-language articles published January 1, 2017-December 31, 2019.
- Evaluated 241 articles for statistical handling of non-independence, attrition, outliers, ordinal data, and multiplicity.
Main Results:
- A majority of articles failed to account for non-independence (74-93%) or animal attrition (78%).
- Ordinal variables were often treated as continuous (37%), outliers were not mentioned (83%), and plots concealed data distribution (51%).
- Statistical choices and transparency did not differ by journal rank or reporting requirements.
Conclusions:
- Statistical misapplication in preclinical behavioral neuroscience research can lead to invalid findings.
- Inadequate reporting obscures statistical errors, impacting the evaluation of translational promise.
- Interventions are needed to improve statistical decision-making in this research area.
