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

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

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

KW - ETIOLOGY

KW - RISK-FACTORS

KW - CAUSAL INFERENCE

KW - VOLUME

KW - BOWLING WORKLOAD

KW - COMMITTEE CONSENSUS STATEMENT

KW - JUMPERS KNEE

KW - ELITE

KW - SHOULDER PAIN

KW - TRAINING LOADS

KW - Humans

KW - Models, Statistical

KW - Athletic Injuries/etiology

KW - Time Factors

KW - Sports Medicine

KW - Physical Conditioning, Human

KW - Biomedical Research

KW - Research Design

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

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 -