- Title
- Time-to-event analysis for sports injury research part 1 : Time-varying exposures
- Creator
- Nielsen, Rasmus; Bertelsen, Michael; Ramskov, Daniel; Møller, Merete; Hulme, Adam; Theisen, Daniel; Finch, Caroline; Fortington, Lauren; Mansournia, Mohammad; Parner, Erik
- Date
- 2019
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/166980
- Identifier
- vital:13531
- Identifier
-
https://doi.org/10.1136/bjsports-2018-099408
- Identifier
- ISBN:0306-3674
- Abstract
- 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. © 2019 Author(s).
- Publisher
- BMJ Publishing Group
- Relation
- British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 61-68
- Rights
- http://creativecommons.org/licenses/by-nc/4.0/
- Rights
- Copyright © 2019 Author(s).
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 09 Engineering; 11 Medical and Health Sciences; 13 Education; Injury; Statistics; Training load
- Full Text
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