Time-to-event analysis for sports injury research part 1: Time-varying exposures

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DOI

  • Rasmus Oestergaard Nielsen
  • Michael Lejbach Bertelsen
  • ,
  • Daniel Ramskov, University College Northern Denmark
  • ,
  • Merete Møller, University of Southern Denmark
  • ,
  • Adam Hulme, University of the Sunshine Coast, Australia
  • Daniel Theisen, Luxembourg Institute of Health, Luxembourg
  • Caroline F. Finch, Edith Cowan University, Joondalup, Australia
  • Lauren Victoria Fortington, Edith Cowan University, Joondalup, University of Ballarat, Australia
  • Mohammad Ali Mansournia, Tehran University of Medical Sciences, Iran, Islamic Republic of
  • Erik Thorlund Parner

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.

Original languageEnglish
JournalBritish Journal of Sports Medicine
Volume53
Issue1
Pages (from-to)61-68
Number of pages8
ISSN0306-3674
DOIs
Publication statusPublished - 1 Jan 2019

    Research areas

  • injury, statistics, training load, ETIOLOGY, RISK-FACTORS, CAUSAL INFERENCE, VOLUME, BOWLING WORKLOAD, COMMITTEE CONSENSUS STATEMENT, JUMPERS KNEE, ELITE, SHOULDER PAIN, TRAINING LOADS, Humans, Models, Statistical, Athletic Injuries/etiology, Time Factors, Sports Medicine, Physical Conditioning, Human, Biomedical Research, Research Design

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