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Assessing remedies for missing weekly individual exposure in sport injury studies
  1. Jian Kang1,
  2. Yan Yuan2,
  3. Carolyn Emery1,3
  1. 1Faculty of Kinesiology, Sport Medicine Centre, Roger Jackson Centre for Health and Wellness Research, University of Calgary, Calgary, Alberta, Canada
  2. 2Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada
  3. 3Department of Community Health Sciences, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada
  1. Correspondence to Dr Jian Kang, Faculty of Kinesiology, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4; kang{at}ucalgary.ca

Abstract

Background In sport injury epidemiology research, the injury incidence rate (IR) is defined as the number of injuries over a given length of participation time (exposure, eg, game hours). However, it is common that individual weekly exposure is missing due to requirements of personnel at every game to record exposure information. Ignoring this issue will lead to an inflated injury rate because the total exposure serves as the denominator of IR, where the number of injury cases were captured accurately.

Purpose This paper used data collected from a large community cohort study in youth ice hockey as an example, and compared six methods to handle missing weekly exposure of individual players.

Methods The six methods to handle missing weekly exposures include available case analysis, last observation carried forward, mean imputation, multiple imputation, bootstrapping and best/worst case analysis. To estimate injury rate ratios (IRRs) between Alberta and Quebec, as in the original study, three statistical models were applied to the imputed datasets: Poisson, zero-inflated Poisson and negative binomial regression models.

Results The final sample for imputation included 2098 players for whom 12.5% of weekly game hours were missing. Estimated IRs and IRRs with CIs from different imputation methods were similar when the proportion of missing was small. Simulations showed that mean and multiple imputations provide the least biased estimates of IRR when the proportion of missing was large.

Conclusions Complicated methods, like multiple imputation or bootstrap, are not superior over the mean imputation, a much simpler method, in handling missing weekly exposure of injury data where exposures were missing at random.

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