Time-to-event analysis for sports injury research part 1 : Time-varying exposures
- Nielsen, Rasmus, Bertelsen, Michael, Ramskov, Daniel, Møller, Merete, Hulme, Adam, Theisen, Daniel, Finch, Caroline, Fortington, Lauren, Mansournia, Mohammad, Parner, Erik
- Authors: 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
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 61-68
- Full Text:
- Reviewed:
- Description: 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).
- Authors: 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
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 61-68
- Full Text:
- Reviewed:
- Description: 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).
Controlled ecological evaluation of an implemented exercise-training programme to prevent lower limb injuries in sport : Population-level trends in hospital-treated injuries
- Finch, Caroline, Gray, Shannon, Akram, Muhammad, Donaldson, Alex, Lloyd, David, Cook, Jill
- Authors: Finch, Caroline , Gray, Shannon , Akram, Muhammad , Donaldson, Alex , Lloyd, David , Cook, Jill
- Date: 2019
- Type: Text , Journal article
- Relation: British Journal of Sports Medicine Vol. 53, no. 8 (2019), p. 487-492
- Full Text:
- Reviewed:
- Description: Objective Exercise-training programmes have reduced lower limb injuries in trials, but their population-level effectiveness has not been reported in implementation trials. This study aimed to demonstrate that routinely collected hospital data can be used to evaluate population-level programme effectiveness. Method A controlled ecological design was used to evaluate the effect of FootyFirst, an exercise-training programme, on the number of hospital-treated lower limb injuries sustained by males aged 16-50 years while participating in community-level Australian Football. FootyFirst was implemented with a € support' (FootyFirst+S) or a € without support' (FootyFirst+NS) in different geographic regions of Victoria, Australia: 22 clubs in region 1: FootyFirst+S in 2012/2013; 25 clubs in region 2: FootyFirst+NS in 2012/2013; 31 clubs region 3: control in 2012, FootyFirst+S in 2013. Interrupted time-series analysis compared injury counts across regions and against trends in the rest of Victoria. Results After 1 year of FootyFirst+S, there was a non-statistically significant decline in the number of lower limb injuries in region 1 (2012) and region 3 (2013); this was not maintained after 2 years in region 1. Compared with before FootyFirst in 2006-2011, injury count changes at the end of 2013 were: region 1: 20.0% reduction (after 2 years support); region 2: 21.5% increase (after 2 years without support); region 3: 21.8% increase (after first year no programme, second year programme with support); rest of Victoria: 12.6% increase. Conclusion Ecological analyses using routinely collected hospital data show promise as the basis of population-level programme evaluation. The implementation and sustainability of sports injury prevention programmes at the population-level remains challenging.
- Authors: Finch, Caroline , Gray, Shannon , Akram, Muhammad , Donaldson, Alex , Lloyd, David , Cook, Jill
- Date: 2019
- Type: Text , Journal article
- Relation: British Journal of Sports Medicine Vol. 53, no. 8 (2019), p. 487-492
- Full Text:
- Reviewed:
- Description: Objective Exercise-training programmes have reduced lower limb injuries in trials, but their population-level effectiveness has not been reported in implementation trials. This study aimed to demonstrate that routinely collected hospital data can be used to evaluate population-level programme effectiveness. Method A controlled ecological design was used to evaluate the effect of FootyFirst, an exercise-training programme, on the number of hospital-treated lower limb injuries sustained by males aged 16-50 years while participating in community-level Australian Football. FootyFirst was implemented with a € support' (FootyFirst+S) or a € without support' (FootyFirst+NS) in different geographic regions of Victoria, Australia: 22 clubs in region 1: FootyFirst+S in 2012/2013; 25 clubs in region 2: FootyFirst+NS in 2012/2013; 31 clubs region 3: control in 2012, FootyFirst+S in 2013. Interrupted time-series analysis compared injury counts across regions and against trends in the rest of Victoria. Results After 1 year of FootyFirst+S, there was a non-statistically significant decline in the number of lower limb injuries in region 1 (2012) and region 3 (2013); this was not maintained after 2 years in region 1. Compared with before FootyFirst in 2006-2011, injury count changes at the end of 2013 were: region 1: 20.0% reduction (after 2 years support); region 2: 21.5% increase (after 2 years without support); region 3: 21.8% increase (after first year no programme, second year programme with support); rest of Victoria: 12.6% increase. Conclusion Ecological analyses using routinely collected hospital data show promise as the basis of population-level programme evaluation. The implementation and sustainability of sports injury prevention programmes at the population-level remains challenging.
Time-to-event analysis for sports injury research part 2 : Time-varying outcomes
- Nielsen, Rasmus, Bertelsen, Michael, Ramskov, Daniel, Møller, Merete, Hulme, Adam, Theisen, Daniel, Finch, Caroline, Fortington, Lauren, Mansournia, Mohammad, Parner, Erik
- Authors: 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
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 70-78
- Full Text:
- Reviewed:
- Description: Background: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. Content: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. Conclusion: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.
- Authors: 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
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 70-78
- Full Text:
- Reviewed:
- Description: Background: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. Content: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. Conclusion: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.
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