Bayesian Inference in Sports Injury Epidemiology: Enhancing Practical Application and Interpretation

Background: Sports injury surveillance programs have been vital in advancing understanding of injury epidemiology across diverse athlete populations. Surveillance-based epidemiological measures of injury occurrence are ubiquitous in sports medicine literature, with injury rates representing one of the most commonly used measures. Traditional approaches to calculating injury rates have predominantly relied on frequentist methods, which, while informative, possess inherent limitations in addressing practical questions such as the probability of specific outcomes and the intuitive interpretation of uncertainty. These limitations may constrain the translation of epidemiological findings into meaningful clinical and practical applications. Methods: This review critically examines the application of Bayesian inference to sports injury epidemiology, contrasting its analytical framework with traditional frequentist approaches. Key analytical outputs including credible intervals are compared with their frequentist counterparts. Through simulated and real-world examples, the types of analyses and inferences uniquely enabled by the Bayesian framework are demonstrated. Computational and inferential advantages are systematically evaluated. Results: Bayesian methods offer several practical advantages for sports injury epidemiology. They allow for direct calculation of probabilities for specific outcomes, providing intuitive interpretations of uncertainty that are more accessible to practitioners and decision-makers. The incorporation of prior knowledge enables more efficient use of available data and facilitates updating of estimates as new evidence accumulates. Bayesian credible intervals offer more intuitive interpretations than frequentist confidence intervals, addressing a common source of misunderstanding in applied settings. Hierarchical Bayesian models can better account for the nested structure of sports injury data, improving estimation accuracy for small samples and subgroup analyses. Conclusion: Adoption of Bayesian frameworks can enhance the practical value of sports injury surveillance by providing more intuitive, directly interpretable, and nuanced insights into injury incidence patterns. Future research should focus on developing accessible Bayesian tools and training resources to facilitate wider adoption, and establishing reporting guidelines to ensure transparency and reproducibility in Bayesian sports injury epidemiology.

Keywords:

Bayesian inference , Sports injury , Epidemiology , Injury rates , Credible intervals , Statistical methods