Modeling time loss from sports-related injuries using random effects models: an illustration using soccer-related injury observations

Chandran Avinash (), DiPietro Loretta (), Young Heather () and Elmi Angelo ()
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Chandran Avinash: Director, NCAA Injury Surveillance Program, Datalys Center for Sports Injury Research and Prevention, Indianapolis, IN, USA
DiPietro Loretta: Professor, Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
Young Heather: Professor and Vice Chair, Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
Elmi Angelo: Associate Professor, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington UniversityWashington, DC, USA

Journal of Quantitative Analysis in Sports, 2020, vol. 16, issue 3, 221-235

Abstract: In assessments of sports-related injury severity, time loss (TL) is measured as a count of days lost to injury and analyzed using ordinal cut points. This approach ignores various athlete and event-specific factors that determine the severity of an injury. We present a conceptual framework for modeling this outcome using univariate random effects count or survival regression. Using a sample of US collegiate soccer-related injury observations, we fit random effects Poisson and Weibull Regression models to perform “severity-adjusted” evaluations of TL, and use our models to make inferences regarding the recovery process. Injury site, injury mechanism and injury history emerged as the strongest predictors in our sample. In comparing random and fixed effects models, we noted that the incorporation of the random effect attenuated associations between most observed covariates and TL, and model fit statistics revealed that the random effects models (AICPoisson = 51875.20; AICWeibull-AFT = 51113.00) improved model fit over the fixed effects models (AICPoisson = 160695.20; AICWeibull-AFT = 53179.00). Our analyses serve as a useful starting point for modeling how TL may actually occur when a player is injured, and suggest that random effects or frailty based approaches can help isolate the effect of potential determinants of TL.

Keywords: injury epidemiology; random effects; severity (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/jqas-2019-0030

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