The cost of dichotomising continuous variablesBMJ 2006; 332 doi: https://doi.org/10.1136/bmj.332.7549.1080 (Published 04 May 2006) Cite this as: BMJ 2006;332:1080
- Douglas G Altman, professor of statistics in medicine (firstname.lastname@example.org)1,
- Patrick Royston, professor of statistics2
- 1 Cancer Research UK/NHS Centre for Statistics in Medicine, Wolfson College, Oxford OX2 6UD
- 2 MRC Clinical Trials Unit, London NW1 2DA
- Correspondence to: Professor Altman
Measurements of continuous variables are made in all branches of medicine, aiding in the diagnosis and treatment of patients. In clinical practice it is helpful to label individuals as having or not having an attribute, such as being “hypertensive” or “obese” or having “high cholesterol,” depending on the value of a continuous variable.
Categorisation of continuous variables is also common in clinical research, but here such simplicity is gained at some cost. Though grouping may help data presentation, notably in tables, categorisation is unnecessary for statistical analysis and it has some serious drawbacks. Here we consider the impact of converting continuous data to two groups (dichotomising), as this is the most common approach in clinical research.1
What are the perceived advantages of forcing all individuals into two groups? A common argument is that it greatly simplifies the statistical analysis and leads to easy interpretation and presentation of results. A …
Log in using your username and password
Log in through your institution
Register for a free trial to thebmj.com to receive unlimited access to all content on thebmj.com for 14 days.
Sign up for a free trial