TY - JOUR
T1 - Time-to-event analysis for sports injury research part 1
T2 - Time-varying exposures
AU - Nielsen, Rasmus Oestergaard
AU - Bertelsen, Michael Lejbach
AU - Ramskov, Daniel
AU - Møller, Merete
AU - Hulme, Adam
AU - Theisen, Daniel
AU - Finch, Caroline F.
AU - Fortington, Lauren Victoria
AU - Mansournia, Mohammad Ali
AU - Parner, Erik Thorlund
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - injury
KW - statistics
KW - training load
UR - http://www.scopus.com/inward/record.url?scp=85056374578&partnerID=8YFLogxK
U2 - 10.1136/bjsports-2018-099408
DO - 10.1136/bjsports-2018-099408
M3 - Review article
C2 - 30413422
AN - SCOPUS:85056374578
SN - 0306-3674
VL - 53
SP - 61
EP - 68
JO - British Journal of Sports Medicine
JF - British Journal of Sports Medicine
IS - 1
ER -