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Time-To-Event Analysis for Sports Injury Research Part 2: Time-Varying Outcomes Publisher Pubmed



Nielsen RO1 ; Bertelsen ML1 ; Ramskov D1, 2 ; Moller M3 ; Hulme A4 ; Theisen D5 ; Finch CF6 ; Fortington LV6, 7 ; Mansournia MA8, 9 ; Parner ET10
Authors
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Authors Affiliations
  1. 1. Department of Public Health, Section for Sports Science, Aarhus University, Aarhus, 8000, Denmark
  2. 2. Department of Physiotherapy, University College Northern Denmark, Aalborg, Denmark
  3. 3. Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
  4. 4. Centre for Human Factors and Sociotechnical Systems, Faculty of Arts, Business and Law, University of the Sunshine Coast, Maroochydore DC, QLD, Australia
  5. 5. Sports Medicine Research Laboratory, Luxembourg Institute of Health, Luxembourg, Luxembourg
  6. 6. Australian Centre for Research into Injury in Sport and its Prevention, School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia
  7. 7. Faculty of Science and Technology, Federation University Australia, Ballarat, VIC, Australia
  8. 8. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  9. 9. Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
  10. 10. Department of Public Health, Section for Biostatistics, Aarhus University, Aarhus, Denmark

Source: British Journal of Sports Medicine Published:2019


Abstract

Background: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. Content: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. Conclusion: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward. © 2019 Author(s).
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