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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.

Original publication

DOI

10.1002/sim.2331

Type

Journal article

Journal

Statistics in medicine

Publication Date

01/2006

Volume

25

Pages

127 - 141

Addresses

MRC Clinical Trials Unit, 222 Euston Road, London NW1 2DA, UK. patrick.royston@ctu.mrc.ac.uk

Keywords

Humans, Cholestasis, Liver Cirrhosis, Biliary, Bilirubin, Azathioprine, Albumins, Antimetabolites, Data Interpretation, Statistical, Regression Analysis, Age Factors, Randomized Controlled Trials as Topic