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Activity Number: 407 - Emerging Methods in Sports Injury Research: Strategies for Shifting the Paradigm
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistics in Sports
Abstract #313523
Title: Reconciling the Dynamics Between Sports-Related Injury Severity and Recovery Using Time Loss
Author(s): Avinash Chandran* and Loretta DiPietro and Heather Young and Angelo Elmi
Companies: Datalys Center for Sports Injury Research and Prevention and The George Washington University and The George Washington University and The George Washington University
Keywords: Sports injury; Time loss; Recovery; Injury severity; Mixed effects; Injury surveillance

Time loss (TL) is commonly synonymized with injury severity in sports medicine. However, injury severity is inherently latent, and TL may be better utilized as a reflection of the recovery process by incorporating injury severity in analyses. We propose two approaches for applying the aforementioned framework where injury severity is represented using a random effect term in analyses of TL. Using sample data collected within the NCAA Injury Surveillance Program, we fit random effects Poisson and Weibull AFT Regression models to perform ‘severity-adjusted’ analyses of TL and make inferences regarding injury recovery. In practice, the incorporation of a random effect term attenuated associations between observable covariates and TL. Model fit was also improved in random effects models (AICPoisson= 51870.06; AICWeibull-AFT= 51291.00) compared with fixed effects models (AICPoisson= 160695.20; AICWeibull-AFT= 53179.48). We present a framework for reconciling the dynamics between sports-related injury severity and recovery. This approach can be used in the future to inform nuanced injury recovery and rehabilitation strategies.

Authors who are presenting talks have a * after their name.

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