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Time-to-event analysis for sports injury research part 1: time-varying exposures
  1. Rasmus Oestergaard Nielsen1,
  2. Michael Lejbach Bertelsen1,
  3. Daniel Ramskov1,2,
  4. Merete Møller3,
  5. Adam Hulme4,
  6. Daniel Theisen5,
  7. Caroline F Finch6,
  8. Lauren Victoria Fortington6,7,
  9. Mohammad Ali Mansournia8,9,
  10. Erik Thorlund Parner10
  1. 1 Section for Sports Science, Department of Public Health, Aarhus University, Aarhus, 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, University of the Sunshine Coast, Maroochydore DC, Queensland, 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, Western Australia, Australia
  7. 7 Faculty of Science and Technology, Federation University Australia, Ballarat, Victoria, 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 Section for Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark
  1. Correspondence to Dr Rasmus Oestergaard Nielsen, Section for Sports Science, Department of Public Health, Aarhus University, Aarhus 8000, Denmark; roen{at}


Background ‘How much change in training load is too much before injury is sustained, among different athletes?’ is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology.

Aim To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes.

Content Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills.

Conclusion To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data.

  • injury
  • training load
  • statistics

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:

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  • Contributor RON drafted the manuscript and all authors revised it for important intellectual content. MAM and EP drafted the section about causal inference.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent Obtained.

  • Ethics approval Local ethics committee in the central Denmark region.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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    Rasmus Oestergaard Nielsen Michael Lejbach Bertelsen Daniel Ramskov Merete Møller Adam Hulme Daniel Theisen Caroline F Finch Lauren Victoria Fortington Mohammad Ali Mansournia Erik Thorlund Parner