Dichotomizing continuous predictors in multiple regression: a bad idea.
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.
Royston
Patrick
P
MRC Clinical Trials Unit, 222 Euston Road, London NW1 2DA, UK. patrick.royston@ctu.mrc.ac.uk
Altman
Douglas G
DG
Sauerbrei
Willi
W
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
D000418:Albumins; D000963:Antimetabolites; D001379:Azathioprine; D001663:Bilirubin