Time-to-event analysis for sports injury research part 2:time-varying outcomes
| Autor: | Adam Hulme, Michael Lejbach Bertelsen, Caroline F. Finch, Daniel Theisen, Lauren V Fortington, Mohammad Ali Mansournia, Rasmus Nielsen, Daniel Ramskov, Merete Møller, Erik T. Parner |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Research design
Risk medicine.medical_specialty Sports injury Time Factors Biomedical Research Sports medicine Athletic Injuries/etiology injury Physical Therapy Sports Therapy and Rehabilitation Sports Medicine Stratified analysis 03 medical and health sciences STANDARD 0302 clinical medicine medicine EPIDEMIOLOGY Humans Orthopedics and Sports Medicine LOAD 030212 general & internal medicine Training load Survival analysis Data collection Actuarial science Models Statistical biology Athletes Education Reviews 030229 sport sciences General Medicine training load RANDOMIZED CONTROLLED-TRIAL FRAMEWORK biology.organism_classification COMPETING RISKS ETIOLOGY statistics Research Design CAUSAL INFERENCE RUNNERS Athletic Injuries Psychology SHOES |
| Zdroj: | Nielsen, R Ø, Bertelsen, M L, Ramskov, D, Møller, M, Hulme, A, Theisen, D, Finch, C F, Fortington, L V, Mansournia, M A & Parner, E T 2019, ' Time-to-event analysis for sports injury research part 2 : time-varying outcomes ', British Journal of Sports Medicine, vol. 53, no. 1, pp. 70-78 . https://doi.org/10.1136/bjsports-2018-100000 British Journal of Sports Medicine Nielsen, R O, Bertelsen, M L, Ramskov, D, Møller, M, Hulme, A, Theisen, D, Finch, C F, Fortington, L V, Mansournia, M A & Parner, E T 2019, ' Time-to-event analysis for sports injury research part 2 : Time-varying outcomes ', British Journal of Sports Medicine, vol. 53, no. 1, pp. 70-78 . https://doi.org/10.1136/bjsports-2018-100000 |
| DOI: | 10.1136/bjsports-2018-100000 |
| Popis: | BackgroundTime-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.ContentIn 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.ConclusionTime-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. |
| Databáze: | OpenAIRE |
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