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Statistical modelling for recurrent events: an application to sports injuries
  1. Shahid Ullah1,
  2. Tim J Gabbett2,3,
  3. Caroline F Finch4
  1. 1Flinders Centre for Epidemiology and Biostatistics, Faculty of Health Sciences, Flinders University, Adelaide, South Australia, Australia
  2. 2School of Exercise Science, Australian Catholic University, Brisbane, Queensland, Australia
  3. 3School of Human Movement Studies, The University of Queensland, Brisbane, Queensland, Australia
  4. 4Australian Centre for Research into Injury in Sport and its Prevention (ACRISP), Monash Injury Research Institute (MIRI), Monash University, Melbourne, Victoria, Australia
  1. Correspondence to Professor Caroline F Finch, Australian Centre for Research into Sports Injury and its Prevention (ACRISP), Monash Injury Research Institute (MIRI), Building 70, Monash University Clayton Campus, Melbourne, VIC 3800, Australia; caroline.finch{at}monash.edu

Abstract

Background Injuries are often recurrent, with subsequent injuries influenced by previous occurrences and hence correlation between events needs to be taken into account when analysing such data.

Objective This paper compares five different survival models (Cox proportional hazards (CoxPH) model and the following generalisations to recurrent event data: Andersen-Gill (A-G), frailty, Wei-Lin-Weissfeld total time (WLW-TT) marginal, Prentice-Williams-Peterson gap time (PWP-GT) conditional models) for the analysis of recurrent injury data.

Methods Empirical evaluation and comparison of different models were performed using model selection criteria and goodness-of-fit statistics. Simulation studies assessed the size and power of each model fit.

Results The modelling approach is demonstrated through direct application to Australian National Rugby League recurrent injury data collected over the 2008 playing season. Of the 35 players analysed, 14 (40%) players had more than 1 injury and 47 contact injuries were sustained over 29 matches. The CoxPH model provided the poorest fit to the recurrent sports injury data. The fit was improved with the A-G and frailty models, compared to WLW-TT and PWP-GT models.

Conclusions Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving modelling of recurrent sports injury data use the frailty model in preference to the CoxPH model or its other generalisations. The paper provides a rationale for future statistical modelling approaches for recurrent sports injury.

  • Statistical review
  • Sporting injuries
  • Rugby

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