Assessment of assumptions of statistical analysis methods in randomised clinical trials: The what and how

Research output: Contribution to journalJournal articleResearchpeer-review

  • Anders Kehlet Nørskov
  • Lange, Theis
  • Emil Eik Nielsen
  • Christian Gluud
  • Per Winkel
  • Jan Beyersmann
  • Jacobo De Uña-Álvarez
  • Valter Torri
  • Laurent Billot
  • Hein Putter
  • Jørn Wetterslev
  • Lehana Thabane
  • Janus Christian Jakobsen

When analysing and presenting results ofrandomised clinical trials, trialists rarely report if or how underlying statisticalassumptions 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 onhow trialists should assess and report underlying assumptions for the analysesof randomised clinical trials. With this study, we developed suggestions on howto test and validate underlying assumptions behind logistic regression, linearregression, and Cox regression when analysing results of randomised clinicaltrials. Two investigators compiled an initial draft based 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) whichunderlying statistical assumptions behind logistic regression, multiple linear regression and Cox regressioneach 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.

Original languageEnglish
Article number111268
JournalBMJ Evidence-Based Medicine
Issue number3
Publication statusPublished - 2021

    Research areas

  • epidemiology, statistics & research methods

ID: 239624653