Dichotomizing continuous predictors in multiple regression: a bad idea
Article first published online: 11 OCT 2005
Copyright © 2005 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 25, Issue 1, pages 127–141, 15 January 2006
How to Cite
Royston, P., Altman, D. G. and Sauerbrei, W. (2006), Dichotomizing continuous predictors in multiple regression: a bad idea. Statist. Med., 25: 127–141. doi: 10.1002/sim.2331
- Issue published online: 10 DEC 2005
- Article first published online: 11 OCT 2005
- Manuscript Accepted: 11 MAY 2005
- Manuscript Received: 4 JAN 2005
- continuous covariates;
- clinical research
In medical research, continuous variables are often converted into categorical variables by grouping values into two or more categories. We consider in detail issues pertaining to creating just two groups, a common approach in clinical research. We argue that the simplicity achieved is gained at a cost; dichotomization may create rather than avoid problems, notably a considerable loss of power and residual confounding. In addition, the use of a data-derived ‘optimal’ cutpoint leads to serious bias. We illustrate the impact of dichotomization of continuous predictor variables using as a detailed case study a randomized trial in primary biliary cirrhosis. Dichotomization of continuous data is unnecessary for statistical analysis and in particular should not be applied to explanatory variables in regression models. Copyright © 2005 John Wiley & Sons, Ltd.