title: Dichotomizing continuous predictors in multiple regression: a bad idea.
abstract: 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.
authors.affiliation: MRC Clinical Trials Unit, 222 Euston Road, London NW1 2DA, UK. patrick.royston@ctu.mrc.ac.uk
authors.forename: Patrick
authors.initials: P
authors.lastname: Royston
authors.forename: Douglas G
authors.initials: DG
authors.lastname: Altman
authors.forename: Willi
authors.initials: W
authors.lastname: Sauerbrei
mesh_terms: D000367:Age Factors; D000418:Albumins / Q000032:analysis; D000963:Antimetabolites / Q000494:pharmacology; D001379:Azathioprine / Q000494:pharmacology; D001663:Bilirubin / Q000032:analysis; D002779:Cholestasis / Q000188:drug therapy; D003627:Data Interpretation, Statistical; D006801:Humans; D008105:Liver Cirrhosis, Biliary / Q000188:drug therapy; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012044:Regression Analysis
chemical_list: D000418:Albumins; D000963:Antimetabolites; D001379:Azathioprine; D001663:Bilirubin