IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v16y2020i3p221-235n3.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chandran Avinash & DiPietro Loretta & Young Heather & Elmi Angelo, 2020. "Modeling time loss from sports-related injuries using random effects models: an illustration using soccer-related injury observations," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(3), pages 221-235, September.
    • Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:3:p:221-235:n:3
    DOI: 10.1515/jqas-2019-0030
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2019-0030
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jqas-2019-0030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chris Elbers & Geert Ridder, 1982. "True and Spurious Duration Dependence: The Identifiability of the Proportional Hazard Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(3), pages 403-409.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peng, Yingwei & Zhang, Jiajia, 2008. "Identifiability of a mixture cure frailty model," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2604-2608, November.
    2. Mihailo Radoman & Marcel C. Voia, 2015. "Youth Training Programs and Their Impact on Career and Spell Duration of Professional Soccer Players," LABOUR, CEIS, vol. 29(2), pages 163-193, June.
    3. Drepper, Bettina & Effraimidis, Georgios, 2012. "Nonparametric identification of dynamic treatment effects in competing risks models," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 66060, Verein für Socialpolitik / German Economic Association.
    4. Dorsett, Richard, 2014. "The effect of temporary in-work support on employment retention: Evidence from a field experiment," Labour Economics, Elsevier, vol. 31(C), pages 61-71.
    5. Yashin, Anatoli I. & Arbeev, Konstantin G. & Akushevich, Igor & Kulminski, Alexander & Akushevich, Lucy & Ukraintseva, Svetlana V., 2008. "Model of hidden heterogeneity in longitudinal data," Theoretical Population Biology, Elsevier, vol. 73(1), pages 1-10.
    6. Manitra Rakotoarisoa, 2007. "Explaining Durations in Country Investment Ratings: A Competing Risk Model with Random-Effects," EcoMod2007 23900074, EcoMod.
    7. Brian Clark & Clément Joubert & Arnaud Maurel, 2017. "The career prospects of overeducated Americans," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-29, December.
    8. Rosa L. Matzkin, 2003. "Nonparametric Estimation of Nonadditive Random Functions," Econometrica, Econometric Society, vol. 71(5), pages 1339-1375, September.
    9. Abbring, Jaap H. & van den Berg, Gerard J., 2003. "A Simple Procedure for the Evaluation of Treatment Effects on Duration Variables," IZA Discussion Papers 810, Institute of Labor Economics (IZA).
    10. Jaap H. Abbring, 0000. "Mixed Hitting-Time Models," Tinbergen Institute Discussion Papers 07-057/3, Tinbergen Institute, revised 11 Aug 2009.
    11. Bruno Crépon & Muriel Dejemeppe & Marc Gurgand, 2005. "Counseling the unemployed: does it lower unemployment duration and recurrence?," Working Papers halshs-00590769, HAL.
    12. Bonev, Petyo, 2020. "Nonparametric identification in nonseparable duration models with unobserved heterogeneity," Economics Working Paper Series 2005, University of St. Gallen, School of Economics and Political Science.
    13. Mette Gerster & Niels Keiding & Lisbeth B. Knudsen & Katrine Strandberg-Larsen, 2007. "Education and second birth rates in Denmark 1981-1994," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 17(8), pages 181-210.
    14. Eric Gautier & Erwann Le Pennec, 2011. "Adaptive Estimation in the Nonparametric Random Coefficients Binary Choice Model by Needlet Thresholding," Working Papers 2011-20, Center for Research in Economics and Statistics.
    15. Gaure, Simen & Roed, Knut & Zhang, Tao, 2007. "Time and causality: A Monte Carlo assessment of the timing-of-events approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1159-1195, December.
    16. Guido Imbens & Lisa Lynch, 2006. "Re-employment probabilities over the business cycle," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 5(2), pages 111-134, August.
    17. Guillaume Horny & Dragana Djurdjevic & Bernhard Boockmann & François Laisney, 2008. "Bayesian Estimation of Cox Models with Non-nested Random Effects: an Application to the Ratification Of ILO Conventions by Developing Countries," Annals of Economics and Statistics, GENES, issue 89, pages 193-214.
    18. Pieter Serneels, 2004. "The Nature of Unemployment in Urban Ethiopia," CSAE Working Paper Series 2004-01, Centre for the Study of African Economies, University of Oxford.
    19. Abbring, Jaap H., 2002. "Stayers versus defecting movers: a note on the identification of defective duration models," Economics Letters, Elsevier, vol. 74(3), pages 327-331, February.
    20. Hilary W. Hoynes & Kenneth Y. Chay & Dean Hyslop, 2004. "True State Dependence In Monthly Welfare Participation:A Nonexperimental Analysis," Working Papers 2, University of California, Davis, Department of Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:16:y:2020:i:3:p:221-235:n:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.