Problems of correlations between explanatory variables in multiple regression analyses in the dental literature

Br Dent J. 2005 Oct 8;199(7):457-61. doi: 10.1038/sj.bdj.4812743.


Multivariable analysis is a widely used statistical methodology for investigating associations amongst clinical variables. However, the problems of collinearity and multicollinearity, which can give rise to spurious results, have in the past frequently been disregarded in dental research. This article illustrates and explains the problems which may be encountered, in the hope of increasing awareness and understanding of these issues, thereby improving the quality of the statistical analyses undertaken in dental research. Three examples from different clinical dental specialties are used to demonstrate how to diagnose the problem of collinearity/multicollinearity in multiple regression analyses and to illustrate how collinearity/multicollinearity can seriously distort the model development process. Lack of awareness of these problems can give rise to misleading results and erroneous interpretations. Multivariable analysis is a useful tool for dental research, though only if its users thoroughly understand the assumptions and limitations of these methods. It would benefit evidence-based dentistry enormously if researchers were more aware of both the complexities involved in multiple regression when using these methods and of the need for expert statistical consultation in developing study design and selecting appropriate statistical methodologies.

MeSH terms

  • Data Interpretation, Statistical*
  • Dental Research / statistics & numerical data*
  • Humans
  • Models, Statistical*
  • Multivariate Analysis
  • Principal Component Analysis
  • Regression Analysis*