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Distinguishing between causal and non-causal associations: implications for sports medicine clinicians
  1. Steven D Stovitz1,
  2. Evert Verhagen2,
  3. Ian Shrier3
  1. 1Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, Minnesota, USA
  2. 2Amsterdam Collaboration on Health & Safety in Sports and Department of Public and Occupational Health, Amsterdam Movement Science, VU University Medical Center, Amsterdam, The Netherlands
  3. 3Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
  1. Correspondence to Dr Steven D Stovitz, Department of Family Medicine and Community Health, University of Minnesota, 420 Delaware St, SEMMC 381, Minneapolis, MN 55455, USA; stovitz{at}umn.edu

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In any research study, variables may be associated due to either ‘cause and effect’ or alternative reasons that are not causal. While all causal relationships are associational, not all associational relationships are causal, that is, correlation does not equal causation. Sports medicine clinicians are generally interested in causal relationships because they want to know whether an intervention will prevent, treat or rehabilitate injury. However, many sport medicine studies do not distinguish between causal and non-causal associations, although they may present their findings in a manner that implies causation. The purpose of this editorial is to help clinicians distinguish causal and non-causal associations to avoid faulty conclusions and misguided clinical decisions.

Generally, in a well-conducted randomised trial with a sufficient sample size, high adherence and minimal dropouts, one can assume that the change in the outcome was caused by the treatment. However, in observational studies, there are often additional hidden assumptions to be acknowledged before concluding the relationship is causal; for example, the absence of confounding or collider stratification bias. Causal diagrams such as those in figure 1 make many of these assumptions explicit.1 In addition, when random study results are absurd, such as an association between a sports team logo and concussion risk,2 a causal diagram forces researchers to either explain the association or to label it as a chance finding. Shrier and …

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