scholarly journals Time-to-event analysis for sports injury research part 2: time-varying outcomes

2018 ◽  
Vol 53 (1) ◽  
pp. 70-78 ◽  
Author(s):  
Rasmus Oestergaard Nielsen ◽  
Michael Lejbach Bertelsen ◽  
Daniel Ramskov ◽  
Merete Møller ◽  
Adam Hulme ◽  
...  
Keyword(s):  
Sports Injuries ◽  
Sports Injury ◽  
Training Load ◽  
Future Research ◽  
Multiple Time ◽  
Time Varying ◽  
Event Analysis ◽  
Time To Event ◽  
Injury Research ◽  
Stratified Analysis

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.

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

2018 ◽  
Vol 53 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Rasmus Oestergaard Nielsen ◽  
Michael Lejbach Bertelsen ◽  
Daniel Ramskov ◽  
Merete Møller ◽  
Adam Hulme ◽  
...  
Keyword(s):  
Sport Injury ◽  
Sports Injury ◽  
Training Load ◽  
Time Varying ◽  
Event Analysis ◽  
Time To Event ◽  
Effect Measure ◽  
Event Models ◽  
Analytical Approaches ◽  
Over Time

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.AimTo discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes.ContentTime-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.ConclusionTo 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.

scholarly journals Masking in reports of “most serious” events: bias in estimators of sports injury incidence in Canadian children

2016 ◽  
Vol 36 (8) ◽  
pp. 143-148 ◽  
Author(s):  
A. Gupta ◽  
C. M. Davison ◽  
M. A. McIsaac
Keyword(s):  
Health Care Providers ◽  
Sports Injuries ◽  
Sports Injury ◽  
Future Research ◽  
Injury Incidence ◽  
Care Providers ◽  
Survey Length ◽  
Serious Injury ◽  
Injury Reporting ◽  
The One

Introduction Surveys that collect information on injuries often focus on the single “most serious” event to help limit recall error and reduce survey length. However, this can mask less serious injuries and result in biased incidence estimates for specific injury subcategories. Methods Data from the 2002 Health Behaviour in School-aged Children (HBSC) survey and from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) were used to compare estimates of sports injury incidence in Canadian children. Results HBSC data indicate that 6.7% of children report sustaining a sports injury that required an emergency department (ED) visit. However, details were only collected on a child’s “most serious” injury, so children who had multiple injuries requiring an ED visit may have had sports injuries that went unreported. The rate of 6.7% can be seen to be an underestimate by as much as 4.3%. Corresponding CHIRPP surveillance data indicate an incidence of 9.9%. Potential masking bias is also highlighted in our analysis of injuries attended by other health care providers. Conclusion The “one most serious injury” line of questioning induces potentially substantial masking bias in the estimation of sports injury incidence, which limits researchers’ ability to quantify the burden of sports injury. Longer survey recall periods naturally lead to greater masking. The design of future surveys should take these issues into account. In order to accurately inform policy decisions and the direction of future research, researchers must be aware of these limitations.

Sports Biomechanical Research and Exploration on the Tennis Injuries

2014 ◽  
Vol 494-495 ◽  
pp. 301-304
Author(s):  
Hui Hui Hong ◽  
Liang Han
Keyword(s):  
Experimental Study ◽  
Research Methods ◽  
Sports Injuries ◽  
Sports Injury ◽  
Theoretical Research ◽  
Technical Features ◽  
Injury Research ◽  
Biological Measurement ◽  
Sports Biomechanics

Tennis is very popular, but the incorrect action produces sports injuries. The knowledge of sports biomechanics is used in sports injury research on tennis. Two mainly classical research methods on tennis sports biomechanics are theoretical research and experimental study. This paper introduces the technical features of two research methods, theoretical research is mainly based on the model. The experimental study is mainly based on three systems of the kinematics, dynamics and biological measurement. The differences among them are compared in order to provide the overall ideas for study on sports injury of tennis.

scholarly journals Is motivation important to brook trout passage through culverts?

2017 ◽  
Vol 74 (6) ◽  
pp. 885-893 ◽  
Author(s):  
Elsa Goerig ◽  
Theodore Castro-Santos
Keyword(s):  
Brook Trout ◽  
Recurrent Events ◽  
Salvelinus Fontinalis ◽  
Time Varying ◽  
Event Analysis ◽  
Dynamic Phenomenon ◽  
Time To Event ◽  
Spawning Period ◽  
Restrict Movement ◽  
Time Varying Covariates

Culverts can restrict movement of stream-dwelling fish. Motivation to enter and ascend these structures is an essential precursor for successful passage. However, motivation is challenging to quantify. Here, we use attempt rate to assess motivation of 447 brook trout (Salvelinus fontinalis) entering three culverts under a range of hydraulic, environmental, and biological conditions. A passive integrated transponder system allowed for the identification of passage attempts and success of individual fish. Attempt rate was quantified using time-to-event analysis allowing for time-varying covariates and recurrent events. Attempt rate was greatest during the spawning period, at elevated discharge, at dusk, and for longer fish. It decreased during the day and with increasing number of conspecifics downstream of the culvert. Results also show a positive correlation between elevated motivation and successful passage. This study enhances understanding of factors influencing brook trout motivation to ascend culverts and shows that attempt rate is a dynamic phenomenon, variable over time and among individuals. It also presents methods that could be used to investigate other species’ motivation to pass natural or anthropogenic barriers.

scholarly journals Strength training as superior, dose-dependent and safe prevention of acute and overuse sports injuries: a systematic review, qualitative analysis and meta-analysis

2018 ◽  
Vol 52 (24) ◽  
pp. 1557-1563 ◽  
Author(s):  
Jeppe Bo Lauersen ◽  
Thor Einar Andersen ◽  
Lars Bo Andersen
Keyword(s):  
Systematic Review ◽  
Qualitative Analysis ◽  
Strength Training ◽  
Publication Bias ◽  
Sports Injuries ◽  
Meta Analysis ◽  
Sports Injury ◽  
Future Research ◽  
Training Volume ◽  
Strength Of Evidence

ObjectiveThis review aims to analyse strength training-based sports injury prevention randomised controlled trials (RCT) and present best evidence recommendations for athletes and future research. A priori PROSPERO registration; CRD42015006970.DesignSystematic review, qualitative analysis and meta-analysis. Sorting of studies and quality assessments were performed by two independent authors. Qualitative analyses, relative risk (RR) estimate with robustness and strength of evidence tests, formal tests of publication bias and post-hoc meta-regression were performed.Data sourcesPubMed, Embase, Web of Science and SPORTDiscus were searched to July 2017.Eligibility criteria for selecting studiesRCTs on strength training exercises as primary prevention of sports injuries.ResultsSix studies analysed five different interventions with four distinct outcomes. 7738 participants aged 12–40 years were included and sustained 177 acute or overuse injuries. Studies were published in 2003–2016, five from Europe and one from Africa. Cluster-adjusted intention-to-treat analysis established RR 0.338 (0.238–0.480). The result was consistent across robustness tests and strength of evidence was high. A 10% increase in strength training volume reduced the risk of injury by more than four percentage points. Formal tests found no publication bias.ConclusionThe included studies were generally well designed and executed, had high compliance rates, were safe, and attained consistently favourable results across four different acute and overuse injury outcomes despite considerable differences in populations and interventions. Increasing strength training volume and intensity were associated with sports injury risk reduction. Three characteristically different approaches to prevention mechanisms were identified and incorporated into contemporary strength training recommendations.

scholarly journals Optimising assessment of dark adaptation data using time to event analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bethany E. Higgins ◽  
Giovanni Montesano ◽  
Alison M. Binns ◽  
David P. Crabb
Keyword(s):  
Censored Data ◽  
Dark Adaptation ◽  
Age Related Macular Degeneration ◽  
Event Analysis ◽  
Sample Sizes ◽  
Time To Event ◽  
Recording Time ◽  
Age Related ◽  
Data Points ◽  
Adaptation Data

AbstractIn age-related macular degeneration (AMD) research, dark adaptation has been found to be a promising functional measurement. In more severe cases of AMD, dark adaptation cannot always be recorded within a maximum allowed time for the test (~ 20–30 min). These data are recorded either as censored data-points (data capped at the maximum test time) or as an estimated recovery time based on the trend observed from the data recorded within the maximum recording time. Therefore, dark adaptation data can have unusual attributes that may not be handled by standard statistical techniques. Here we show time-to-event analysis is a more powerful method for analysis of rod-intercept time data in measuring dark adaptation. For example, at 80% power (at α = 0.05) sample sizes were estimated to be 20 and 61 with uncapped (uncensored) and capped (censored) data using a standard t-test; these values improved to 12 and 38 when using the proposed time-to-event analysis. Our method can accommodate both skewed data and censored data points and offers the advantage of significantly reducing sample sizes when planning studies where this functional test is an outcome measure. The latter is important because designing trials and studies more efficiently equates to newer treatments likely being examined more efficiently.

scholarly journals Time-to-event analysis in economic evaluations: a comparison of modelling methods to assess the cost-effectiveness of transplanting a marginal quality kidney

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sameera Senanayake ◽  
Nicholas Graves ◽  
Helen Healy ◽  
Keshwar Baboolal ◽  
Adrian Barnett ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Time Horizon ◽  
Transition Probabilities ◽  
Discrete Event ◽  
Economic Evaluations ◽  
Event Analysis ◽  
Time To Event ◽  
Modelling Method ◽  
Cycle Lengths ◽  
Modelling Methods

Abstract Background Economic-evaluations using decision analytic models such as Markov-models (MM), and discrete-event-simulations (DES) are high value adds in allocating resources. The choice of modelling method is critical because an inappropriate model yields results that could lead to flawed decision making. The aim of this study was to compare cost-effectiveness when MM and DES were used to model results of transplanting a lower-quality kidney versus remaining waitlisted for a kidney. Methods Cost-effectiveness was assessed using MM and DES. We used parametric survival models to estimate the time-dependent transition probabilities of MM and distribution of time-to-event in DES. MMs were simulated in 12 and 6 monthly cycles, out to five and 20-year time horizon. Results DES model output had a close fit to the actual data. Irrespective of the modelling method, the cycle length of MM or the time horizon, transplanting a low-quality kidney as compared to remaining waitlisted was the dominant strategy. However, there were discrepancies in costs, effectiveness and net monetary benefit (NMB) among different modelling methods. The incremental NMB of the MM in the 6-months cycle lengths was a closer fit to the incremental NMB of the DES. The gap in the fit of the two cycle lengths to DES output reduced as the time horizon increased. Conclusion Different modelling methods were unlikely to influence the decision to accept a lower quality kidney transplant or remain waitlisted on dialysis. Both models produced similar results when time-dependant transition probabilities are used, most notable with shorter cycle lengths and longer time-horizons.

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