Statistical Methods for Handling Observation Clustering in Sports Injury Surveillance

Advances in sports injury-surveillance methods have made it possible to accommodate non-time-loss (NTL) injury reporting; however, the analysis of surveillance data now requires careful consideration of the nuances of NTL injury records.

Injury-surveillance mechanisms that record NTL injuries are more likely to contain multiple injury records per athlete. These must be handled appropriately in statistical analyses to make methodologically sound inferences.

We simulated datasets of NTL injuries using varying degrees of observation clustering and compared the inferences made using traditional techniques with those made after accounting for clustering in computations of injury proportion ratios.

Inappropriate handling of even moderate clustering resulted in flawed inferences in 10% to 12% of our simulations. We observed greater bias in our estimates as the degree of clustering increased.

We urge investigators to carefully consider observation clustering and adapt analytical methods to accommodate the evolving sophistication of surveillance.