Background: Dichotomisation of continuous variables before analysis has frequently been criticised but, nonetheless, remains a common approach. We were interested in the effects of dichotomisation of an outcome variable when two predictors are examined.
Methods: Assuming a log-normally distributed continuous outcome, a three-level and a binary independent variable, we evaluated the results that would be obtained by logistic regression after dichotomisation. Different cut-offs, predictor effects and dispersions were examined, with a special focus on interaction terms.
Results: Depending on the specific parameter combination, dichotomisation introduced sometimes substantial spurious interactions between the two predictor variables regarding their association with the outcome. These interactions could be assigned statistical significance even with modest sample sizes. Real-life data on sexxweight as determinants of gamma-glutamyltransferase provided a practical example of these issues.
Conclusions: The findings presented add a new aspect to the controversy surrounding dichotomisation of continuous variables. Researchers should critically examine whether the validity of their results might be hampered by such phenomena.