2018
DOI: 10.1136/bjsports-2018-100000
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Time-to-event analysis for sports injury research part 2: time-varying outcomes

Abstract: 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)… Show more

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Cited by 49 publications
(50 citation statements)
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“…In effect, therefore, the severity score represents an ordinal-scale variable with 25 possible outcomes, not 100. Recent publications highlight the analytical benefits of representing various ‘states’ of an athlete’s health on an ordinal scale 16. Unfortunately, for this approach to be feasible with small samples (as is normally the case in sports medicine research), the number of potential states needs to be far fewer than 25 to reduce the risk of sparse data bias 17.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In effect, therefore, the severity score represents an ordinal-scale variable with 25 possible outcomes, not 100. Recent publications highlight the analytical benefits of representing various ‘states’ of an athlete’s health on an ordinal scale 16. Unfortunately, for this approach to be feasible with small samples (as is normally the case in sports medicine research), the number of potential states needs to be far fewer than 25 to reduce the risk of sparse data bias 17.…”
Section: Discussionmentioning
confidence: 99%
“…Such analyses are of special interest for elite athletic populations that, in general, are smaller in number but tend to experience multiple events (injuries and/or illnesses). When considering longitudinal analytical methods, users should be aware of the inherent challenges of these methods, including (i) missing data, (ii) time-varying exposures, outcomes, confounders, effect-measure modifiers and mediators, (iii) recurrent/subsequent events and (iv) competing risks 16 18 19…”
Section: Discussionmentioning
confidence: 99%
“…20 Survival analyses using the Kaplan-Meier method were conducted to estimate the cumulative survival probability (SP) and the primary endpoints: median time to the first injury and illness, respectively. 22 Log-rank tests assessing the hazard function were used to compare differences in survival times between the subgroups, and Cox proportional hazard regression with corresponding hazard ratios (HR) were performed to analyze the actual risk of sustaining a first SRIIPS. 18 Univariate models assessing risks associated with each variable were first tested.…”
Section: Discussionmentioning
confidence: 99%
“…The median number of reported illnesses per athlete was 2 (IQR 1-4, min-max 0-11) (Table 4). Vertebral column 40 (22) 27 (25) 5 (19) 6 (35) 5 4611 (20) 11 (18) 2 (33) 17 (22) 24 (…”
Section: Illnesses Incidence Ratesmentioning
confidence: 99%
“…This approach was introduced based on experiences using the Yamato et al 52 consensus definition and the Oslo Trauma Research Center questionnaire 53. Importantly, the injury outcome should be considered as a time-varying covariate as described in Nielsen et al 54…”
Section: Methods and Analysismentioning
confidence: 99%