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Methods matter: instrumental variable analysis may be a complementary approach to intention-to-treat analysis and as treated analysis when analysing data from sports injury trials
  1. Pascal Edouard1,2,3,4,5,
  2. Kathrin Steffen6,
  3. Laurent Navarro7,
  4. Mohammad Ali Mansournia8,
  5. Rasmus Oestergaard Nielsen9,10
  1. 1Inter‐university Laboratory of Human Movement Science (LIBM EA 7424), University of Lyon, University Jean Monnet, Saint-Etienne, France
  2. 2Department of Clinical and Exercise Physiology, Sports Medicine Unit, IRMIS, University Hospital of Saint-Etienne, Saint-Etienne, France
  3. 3European Athletics Medical & Anti Doping Commission, European Athletics Association (EAA), Lausanne, Switzerland
  4. 4Medical Commission, French Athletics Federation (FFA), Paris, France
  5. 5Swiss Olympic Medical Center, Centre de médecine du sport, Division de médecine physique et réadaptation, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
  6. 6Department of Sports Medicine, Norwegian School of Sports Sciences, Oslo Sports Trauma Research Center, Oslo, Norway
  7. 7Mines Saint-Etienne, INSERM, U 1059 Sainbiose, CIS, Univ Lyon, Univ Jean Monnet, Saint-Etienne, France
  8. 8Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  9. 9Department of Public Health, Section for Sports Science, Aarhus University, Aarhus, Denmark
  10. 10Research Unit for General Practice, Aarhus, Denmark
  1. Correspondence to Dr Pascal Edouard, Department of Clinical and Exercise Physiology, Sports Medicine Unit, IRMIS, University Hospital of Saint-Etienne, Saint-Etienne, France; Pascal.Edouard42{at}gmail.com

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Imagine a sports injury researcher claiming: ‘the effect of the injury prevention programme we reported in the trial is unbiased because we analysed according to the intention-to-treat principle’ (ITT). This sounds appealing for clinicians, coaches and athletes. However, before implementing results from such trial, readers should consider whether the athletes in the trial actually complied with the intervention. The appealing message above from the researcher strongly depends on the ‘whereabouts’ of the athletes. Those in the intervention group(s) need to be fully compliant to draw a meaningful conclusion regarding the effect of the intervention.

In studies affected by low compliance, we believe that drawing a conclusion on the effect of an intervention may be misleading as low compliance may bias results if data are analysed according to the ITT principle.1 We encourage clinicians, coaches and athletes to take a sceptical, cautious step back when reading bombastic conclusions in the sports injury literature. Researchers should do their best to deal with low compliance during (1) study design, (2) data collection and (3) when analysing data from sports injury trials. As a part of a BJSM educational series on methods in randomised controlled trials (RCTs), we discuss here an alternative analytical approach to the ITT—‘instrumental variable (IV) analysis’.2

Low compliance is problematic

To illustrate the problem of compliance, we used a data set from an RCT with 40-week follow-up investigating the efficacy of an Athletics Injury Prevention Programme (AIPP) to reduce injury complaints leading to restrictions in athletics participation (PREVATHLE: CPP Ouest II-Angers, number: 2017-A01980-53; ClinicalTrials.gov Identifier: NCT03307434). In this trial, track and field athletes (n=840) were allocated to either a control group (n=391, reference group), who were asked to continue their usual training, or an intervention group (n=449), who were asked to perform the specifically designed AIPP twice a week. Interestingly and thought-provokingly, …

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Footnotes

  • Twitter @PascalEdouard42, @RUNSAFE_Rasmus

  • Correction notice This article has been corrected since it published Online First. The title, table 1 and the conclusion have all been updated. The competing interests and provenance and peer review statements have also been corrected in the online version only.

  • Contributors PE: substantial contributions to the conception and design of the project, implementation of the project, data collection, analysis and interpretation of data, drafting, writing and revising of the manuscript and final approval of the version to be published. KS: substantial contributions to the analyses and interpretation of data, writing and revising of the manuscript, and final approval of the version to be published. LN: substantial contributions to interpretation of data, writing, revising of the manuscript and final approval of the version to be published. MAM: substantial contributions to the analyses and interpretation of data, writing and revising of the manuscript, and final approval of the version to be published. RON: substantial contributions to the conception and design of the manuscript, analysis and interpretation of data, drafting, writing and revising of the manuscript, and final approval of the version to be published.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests Authors PE, KS, MAM and RON are members of the BJSM editorial board.

  • Provenance and peer review Not commissioned; externally peer reviewed.