Collinearity in linear regression is a serious problem in oral health research

Eur J Oral Sci. 2004 Oct;112(5):389-97. doi: 10.1111/j.1600-0722.2004.00160.x.


The aim of this article is to encourage good practice in the statistical analysis of dental research data. Our objective is to highlight the statistical problems of collinearity and multicollinearity. These are among the most common statistical pitfalls in oral health research when exploring the relationship between clinical variables using multiple regression analysis. We hope that this article will show why these problems arise and how they can be avoided and overcome. Examples from the periodontal literature will be used to illustrate how collinearity and multicollinearity can seriously distort the model development process as a result of the phenomenon of mathematical coupling. Knowledge of these problems can help to eliminate misleading results and improve any subsequent interpretations. Regression analyses are useful tools in oral health research when their limitations are recognized. However, care is required in planning and it is worthwhile seeking statistical advice when formulating the study's research questions.

MeSH terms

  • Algorithms
  • Alveolar Bone Loss / surgery
  • Data Interpretation, Statistical
  • Dental Abutments / statistics & numerical data
  • Dental Implants / statistics & numerical data
  • Dental Prosthesis, Implant-Supported / statistics & numerical data
  • Dental Research / statistics & numerical data*
  • Gingival Recession / therapy
  • Guided Tissue Regeneration / statistics & numerical data
  • Humans
  • Linear Models*
  • Periodontal Attachment Loss / therapy
  • Periodontal Pocket / therapy
  • Periodontics / statistics & numerical data


  • Dental Implants