Bayesian methods for estimating injury rates in sport injury epidemiology

Our findings indicate that the Bayesian framework naturally characterizes injury rates by modeling injury counts as outcomes of an underlying data-generation process that explicitly incorporates inherent uncertainty, complementing traditional frequentist approaches. Key benefits of the Bayesian approach in this context are the ability to test model suitability in a variety of methods, and to be able to generate plausible estimates with sparse data.

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Publication Details

Journal
Injury Epidemiology
Published
2025-06-06
DOI
https://doi.org/10.1186/s40621-025-00583-z
Citations
2
Primary Topic
Sports injuries and prevention
Type
article
Field-Weighted Citation Impact
3.44

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article

Bayesian methods for estimating injury rates in sport injury epidemiology

Avinash Chandran, Ben C. Lambert
2 citations
Injury Epidemiology
Sports injuries and prevention
3.44
article

Bayesian methods for estimating injury rates in sport injury epidemiology

Avinash Chandran, Ben C. Lambert
article en
2 citations

Abstract

Our findings indicate that the Bayesian framework naturally characterizes injury rates by modeling injury counts as outcomes of an underlying data-generation process that explicitly incorporates inherent uncertainty, complementing traditional frequentist approaches. Key benefits of the Bayesian approach in this context are the ability to test model suitability in a variety of methods, and to be able to generate plausible estimates with sparse data.

Injury EpidemiologyVol. 12(1)
University of Oxford (GB), Datalys Center for Sports Injury Research and Prevention (US)
National Collegiate Athletic Association
Good health and well-being
Openalex Percentile: Top 9%
Sports injuries and prevention
3.44
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