Past Injury as a Risk Factor: An Illustrative Example Where Appearances Are Deceiving

Abstract

Previous injury is believed to be a causal risk factor for subsequent injury. Using empirical data on circus artists (n = 1,281 artists) between 2004 and 2008 in Montreal, Canada, as a motivating example, the authors use patient vector plots to demonstrate that a bias away from the null must always occur in the typical analyses cited as evidence (i.e., survival analysis, Poisson regression), except in the improbable context where all subjects have the same inherent risk independent of previous injury. In addition, using simulated data, the authors demonstrate that a simple method that conditions on the individual will approximate conclusions from more complex analytical methods. By using the typical analysis of the authors' empirical data, Kaplan-Meier curves and Cox regression suggested increasing injury rates for both the second and third injuries compared with the first injury. However, conditional analyses using a matched population (i.e., time to first, second, and third injuries among artists with 3 or more injuries) showed that injury rates were unchanged for both the second and third injuries compared with the first injury. These results suggest that previous injury should not be evaluated as a causal risk factor unless one conditions on the individual in some way.