A higher sport-related reinjury risk does not mean inadequate rehabilitation: the methodological challenge of choosing the correct comparison group

Previous injury is a well-established predictor of subsequent injury in sports medicine. Some have interpreted this to mean that either our current methods of rehabilitation are inadequate or there is some permanent damage to the tissue and 100% rehabilitation is not possible. In 2011, we illustrated that these analyses and interpretations failed to account for the fact that some athletes are more prone to get injured, either physiologically, or because of their role/type of play. We suggested that the appropriate analysis would simply require using statistical methods that measured how each individual athlete’s risk changed from preinjury to postinjury. In this paper, we revisit our recommendation and illustrate that it too would be flawed if the risk of injury changed over time independent of an injury ever occurring. This might be expected if general fitness were to decline over the season, or if the style of play changed between early season games and postseason championship games. Acknowledging that risk may change regardless of whether an injury occurred or not leads to three different general definitions of 100% rehabilitation: (1) a return to the baseline state, (2) a return to the immediate preinjury state and (3) a return to the state that would have been present had the initial injury never occurred. We guide the reader on how to estimate the risks for each definition and the assumptions that must be acknowledged.

[1]  B. Lin Nonparametric estimation of the gap time distributions for serial events with censored data , 1999 .

[2]  C. Emery,et al.  What are the Risk Factors for Groin Strain Injury in Sport? , 2007, Sports medicine.

[3]  C. Emery Risk Factors for Injury in Child and Adolescent Sport: A Systematic Review of the Literature , 2003, Clinical journal of sport medicine : official journal of the Canadian Academy of Sport Medicine.

[4]  Zhiliang Ying,et al.  Nonparametric estimation of the gap time distributions for serial events with censored data , 1999 .

[5]  Ian Shrier,et al.  American Journal of Epidemiology Practice of Epidemiology past Injury as a Risk Factor: an Illustrative Example Where Appearances Are Deceiving , 2022 .

[6]  Robert L Strawderman,et al.  Conditional GEE for recurrent event gap times. , 2009, Biostatistics.

[7]  T. Hewett,et al.  A multistate framework for the analysis of subsequent injury in sport (M‐FASIS) , 2016, Scandinavian journal of medicine & science in sports.

[8]  J. Robins,et al.  Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. , 2000, Epidemiology.

[9]  J. Box-Steffensmeier,et al.  Repeated events survival models: the conditional frailty model , 2006, Statistics in medicine.

[10]  B. Beynnon,et al.  Risk factors for lower extremity injury: a review of the literature , 2003, British journal of sports medicine.

[11]  Tim J Gabbett,et al.  Has the athlete trained enough to return to play safely? The acute:chronic workload ratio permits clinicians to quantify a player's risk of subsequent injury , 2015, British Journal of Sports Medicine.