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Assessment of assumptions of statistical analysis methods in randomised clinical trials: the what and how
  1. Anders Kehlet Nørskov1,
  2. Theis Lange2,3,
  3. Emil Eik Nielsen1,4,
  4. Christian Gluud1,
  5. Per Winkel1,
  6. Jan Beyersmann5,
  7. Jacobo de Uña-Álvarez6,
  8. Valter Torri7,
  9. Laurent Billot8,
  10. Hein Putter9,
  11. Jørn Wetterslev1,
  12. Lehana Thabane10,
  13. Janus Christian Jakobsen1,4
  1. 1 Copenhagen Trial Unit, Centre for Clinical Intervention Research, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
  2. 2 Department of Biostatistics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
  3. 3 Centre for Statistical Science, Peking University, Beijing, China
  4. 4 Department of Cardiology, Holbæk Sygehus, Holbæk, Denmark
  5. 5 Institute of Statistics, Universitat Ulm, Ulm, Baden-Württemberg, Germany
  6. 6 Department of Statistics and Operations Research & Biomedical Research Center (CINBIO), Universidade de Vigo, Vigo, Galicia, Spain
  7. 7 IRCCS, Istituto Di Ricerche Farmacologiche Mario Negri, Milano, Italy
  8. 8 The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
  9. 9 Leiden University Medical Center, Universiteit Leiden, Leiden, Zuid-Holland, The Netherlands
  10. 10 Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
  1. Correspondence to Dr Anders Kehlet Nørskov, Copenhagen Trial Unit, Centre for Clinical Intervention Research, Department 7812, Rigshospitalet, Copenhagen 2100, Denmark; anderskehlet{at}


When analysing and presenting results of randomised clinical trials, trialists rarely report if or how underlying statistical assumptions were validated. To avoid data-driven biased trial results, it should be common practice to prospectively describe the assessments of underlying assumptions. In existing literature, there is no consensus on how trialists should assess and report underlying assumptions for the analyses of randomised clinical trials. With this study, we developed suggestions on how to test and validate underlying assumptions behind logistic regression, linear regression, and Cox regression when analysing results of randomised clinical trials.

Two investigators compiled an initial draftbased on a review of the literature. Experienced statisticians and trialists from eight different research centres and trial units then participated in a anonymised consensus process, where we reached agreement on the suggestions presented in this paper.

This paper provides detailed suggestions on 1) which underlying statistical assumptions behind logistic regression, multiple linear regression and Cox regression each should be assessed; 2) how these underlying assumptions may be assessed; and 3) what to do if these assumptions are violated.

We believe that the validity of randomised clinical trial results will increase if our recommendations for assessing and dealing with violations of the underlying statistical assumptions are followed.

  • epidemiology
  • statistics & research methods

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  • Correction notice This article has been corrected since it was published Online First. Minor formatting issues have been corrected, namely in the abstract.

  • Contributors JCJ and TL did the planning and initiated the project. All authors developed the framework and participated in writing the article. AKN is the guarantor of the article. All remaining authors are selected statistical professors, experts, methodologists and trialists from different parts of the world. The recommendations presented in this paper are derived from the authors’ extensive experience and expertise in this field as well as from relevant and up to date literature. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

  • 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 None declared.

  • Patient and public involvement statement We did not involve patients or the public in our work.

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