Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models

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.

Download Full-text

Non-parametric frailty Cox models for hierarchical time-to-event data

Biostatistics ◽

10.1093/biostatistics/kxy071 ◽

2018 ◽

Vol 21 (3) ◽

pp. 531-544 ◽

Cited By ~ 3

Author(s):

Francesca Gasperoni ◽

Francesca Ieva ◽

Anna Maria Paganoni ◽

Christopher H Jackson ◽

Linda Sharples

Keyword(s):

Healthcare Providers ◽

Discrete Distribution ◽

Administrative Database ◽

Model Parameters ◽

Event Data ◽

Time To Event ◽

Cox Models ◽

Time To Event Data ◽

Parametric Frailty ◽

Non Parametric

Summary We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation–Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.

Download Full-text

Local Control and Toxicity of Multilevel Spine Stereotactic Body Radiotherapy

Neurosurgery ◽

10.1093/neuros/nyz348 ◽

2019 ◽

Author(s):

Whitney H Beeler ◽

Kelly A Speth ◽

Michael T Broderick ◽

Neil K Jairath ◽

Dena Ballouz ◽

...

Keyword(s):

Local Control ◽

Stereotactic Body Radiotherapy ◽

Time To Event ◽

Mixed Effect ◽

Cox Models ◽

Single Level ◽

Mixed Effect Models ◽

Random Intercept ◽

Significant Difference ◽

One Year

Abstract BACKGROUND Spine stereotactic body radiotherapy (sSBRT) is commonly limited to 1 or 2 vertebral levels given a paucity of efficacy and toxicity data when more than 2 levels are treated. OBJECTIVE To prove our hypothesis that multilevel sSBRT could provide similar rates of local control (LC) (primary endpoint) and toxicity as single-level treatment using the same clinical target, planning target, and planning organ-at-risk volumes. METHODS We analyzed consecutive cases of sSBRT treated from 2013 to 2017. Time-to-event outcomes for single-level and multilevel cases were compared using mixed effect Cox models and differences in toxicity rates were evaluated using linear mixed effect models. All models incorporate a patient-level random intercept to account for any within-patient correlation across cases. RESULTS There were 101 single-level and 84 multilevel sSBRT cases (2-7 continuous vertebral levels). One-year LC was 95% vs 85%, respectively. After adjusting for baseline covariates, dose delivered, and accounting for within-patient correlation, there was no significant difference in time to local failure (hazard ratio, HR 1.79 [0.59-5.4]; P = .30). Pain improved in 83.5% of the 139 initially symptomatic tumors. There were no significant differences in grade 2+ acute or late toxicities between single-level and multilevel sSBRT. CONCLUSION With rigorous patient immobilization, quality assurance, and image guidance, multilevel sSBRT provides high rates of LC, similar to single-level treatment, without need for larger planning volume margins. Efforts to improve prognostication and case selection for multilevel sSBRT are warranted to ensure that the benefits of improved LC over palliative radiation are justified.

Download Full-text

Shedding Light on the Etiology of Sports Injuries: A Look Behind the Scenes of Time-to-Event Analyses

Journal of Orthopaedic and Sports Physical Therapy ◽

10.2519/jospt.2016.6510 ◽

2016 ◽

Vol 46 (4) ◽

pp. 300-311 ◽

Cited By ~ 39

Author(s):

Rasmus Østergaard Nielsen ◽

Laurent Malisoux ◽

Merete Møller ◽

Daniel Theisen ◽

Erik Thorlund Parner

Keyword(s):

Sports Injuries ◽

Time To Event

Download Full-text

Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies

Journal of Translational Medicine ◽

10.1186/s12967-021-03090-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Haojie Lu ◽

Yongyue Wei ◽

Zhou Jiang ◽

Jinhui Zhang ◽

Ting Wang ◽

...

Keyword(s):

Genetic Variants ◽

Cox Regression ◽

Association Studies ◽

Effect Sizes ◽

Time To Event ◽

Omnibus Test ◽

Cox Models ◽

Functional Annotations ◽

Analysis Strategy ◽

Survival Risk

Abstract Background Integrating functional annotations into SNP-set association studies has been proven a powerful analysis strategy. Statistical methods for such integration have been developed for continuous and binary phenotypes; however, the SNP-set integrative approaches for time-to-event or survival outcomes are lacking. Methods We here propose IEHC, an integrative eQTL (expression quantitative trait loci) hierarchical Cox regression, for SNP-set based survival association analysis by modeling effect sizes of genetic variants as a function of eQTL via a hierarchical manner. Three p-values combination tests are developed to examine the joint effects of eQTL and genetic variants after a novel decorrelated modification of statistics for the two components. An omnibus test (IEHC-ACAT) is further adapted to aggregate the strengths of all available tests. Results Simulations demonstrated that the IEHC joint tests were more powerful if both eQTL and genetic variants contributed to association signal, while IEHC-ACAT was robust and often outperformed other approaches across various simulation scenarios. When applying IEHC to ten TCGA cancers by incorporating eQTL from relevant tissues of GTEx, we revealed that substantial correlations existed between the two types of effect sizes of genetic variants from TCGA and GTEx, and identified 21 (9 unique) cancer-associated genes which would otherwise be missed by approaches not incorporating eQTL. Conclusion IEHC represents a flexible, robust, and powerful approach to integrate functional omics information to enhance the power of identifying association signals for the survival risk of complex human cancers.

Download Full-text

Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies

Statistical Methods in Medical Research ◽

10.1177/0962280216668554 ◽

2016 ◽

Vol 27 (6) ◽

pp. 1709-1722 ◽

Cited By ~ 8

Author(s):

Mohammad Ehsanul Karim ◽

John Petkau ◽

Paul Gustafson ◽

Robert W Platt ◽

Helen Tremlett ◽

...

Keyword(s):

Treatment Time ◽

Causal Effect ◽

Cox Model ◽

Time Dependent ◽

Time To Event ◽

Cox Models ◽

Drug Effectiveness ◽

Time To Event Data ◽

Time Dependent Covariates ◽

The Impact

In longitudinal studies, if the time-dependent covariates are affected by the past treatment, time-dependent confounding may be present. For a time-to-event response, marginal structural Cox models are frequently used to deal with such confounding. To avoid some of the problems of fitting marginal structural Cox model, the sequential Cox approach has been suggested as an alternative. Although the estimation mechanisms are different, both approaches claim to estimate the causal effect of treatment by appropriately adjusting for time-dependent confounding. We carry out simulation studies to assess the suitability of the sequential Cox approach for analyzing time-to-event data in the presence of a time-dependent covariate that may or may not be a time-dependent confounder. Results from these simulations revealed that the sequential Cox approach is not as effective as marginal structural Cox model in addressing the time-dependent confounding. The sequential Cox approach was also found to be inadequate in the presence of a time-dependent covariate. We propose a modified version of the sequential Cox approach that correctly estimates the treatment effect in both of the above scenarios. All approaches are applied to investigate the impact of beta-interferon treatment in delaying disability progression in the British Columbia Multiple Sclerosis cohort (1995–2008).

Download Full-text

Exposure misclassification in propensity score-based time-to-event data analysis

Statistical Methods in Medical Research ◽

10.1177/0962280221998410 ◽

2021 ◽

pp. 096228022199841

Author(s):

Yingrui Yang ◽

Molin Wang

Keyword(s):

Propensity Score ◽

Cox Regression ◽

Asymptotic Properties ◽

Practical Importance ◽

Lung Cancer Mortality ◽

Research Area ◽

Health Study ◽

Exposure Misclassification ◽

Time To Event ◽

Cox Models

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem. In this paper, we propose an estimating equation method to correct for the exposure misclassification-caused bias in the estimation of exposure-outcome associations. We also discuss the asymptotic properties and derive the asymptotic variances of the proposed estimators. We conduct a simulation study to evaluate the performance of the proposed estimators in various settings. As an illustration, we apply our method to correct for the misclassification-caused bias in estimating the association of PM2.5 level with lung cancer mortality using a nationwide prospective cohort, the Nurses’ Health Study. The proposed methodology can be applied using our user-friendly R program published online.

Download Full-text

Continuum of Care Following Sports-Related Concussion

American Journal of Speech-Language Pathology ◽

10.1044/2020_ajslp-19-00131 ◽

2020 ◽

Vol 29 (3) ◽

pp. 1389-1403

Author(s):

Jessica Brown ◽

Kelly Knollman-Porter

Keyword(s):

Young Adults ◽

Life Span ◽

Sports Injuries ◽

Continuum Of Care ◽

Medical Professional ◽

Structured Interviews ◽

Youth Athletes ◽

Speech Language Pathologist ◽

History Of ◽

Injury Reporting

Purpose Although guidelines have changed regarding federally mandated concussion practices since their inception, little is known regarding the implementation of such guidelines and the resultant continuum of care for youth athletes participating in recreational or organized sports who incur concussions. Furthermore, data regarding the role of speech-language pathologists in the historic postconcussion care are lacking. Therefore, the purpose of this retrospective study was to investigate the experiences of young adults with history of sports-related concussion as it related to injury reporting and received follow-up care. Method Participants included 13 young adults with history of at least one sports-related concussion across their life span. We implemented a mixed-methods design to collect both quantitative and qualitative information through structured interviews. Participants reported experiencing 42 concussions across the life span—26 subsequent to sports injuries. Results Twenty-three concussions were reported to a parent or medical professional, 14 resulted in a formal diagnosis, and participants received initial medical care for only 10 of the incidents and treatment or services on only two occasions. Participants reported concussions to an athletic trainer least frequently and to parents most frequently. Participants commented that previous experience with concussion reduced the need for seeking treatment or that they were unaware treatments or supports existed postconcussion. Only one concussion incident resulted in the care from a speech-language pathologist. Conclusion The results of the study reported herein shed light on the fidelity of sports-related concussion care management across time. Subsequently, we suggest guidelines related to continuum of care from injury to individualized therapy.

Download Full-text