Dichotomizing continuous predictors in multiple regression: a bad idea.
127 - 141.
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.
|Title:||Dichotomizing continuous predictors in multiple regression: a bad idea.|
|Keywords:||Age Factors, Albumins, Antimetabolites, Azathioprine, Bilirubin, Cholestasis, Data Interpretation, Statistical, Humans, Liver Cirrhosis, Biliary, Randomized Controlled Trials as Topic, Regression Analysis|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Population Health Sciences > MRC Clinical Trials Unit at UCL|
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