Regression With Binned Variables Today our professor was discussing Sutradhar, Gu and Paszat (2016).  In this paper the authors decided to study the relationships between patient characteristics and following the "advice" of their doctors.
While reading this paper, I noticed (e.g., Page 4) that variables such as "Age" and "Neighborhood Income" are binned into quintiles. I have often read that arbitrary binning (e.g., why quintiles, why not deciles?) of a continuous variable should be avoided as it can both lead to biases as well as phenomena such as "p-hacking". As an example, is it possible that the authors tried different combinations of binning criteria until one of these criteria produced meaningful results?

My question: Ideally speaking, should this "binning" process not have been done and these variables (i.e., age, income) treated as continuous?  Could it be said that this "binning" process might potentially take away from the results in this paper and add biases when compared to treating these variables as continuous?


should this "binning" process not have been done and these variables (i.e., age, income) treated as continuous?

Yes.  Presumably this was done to allow for a non-linear effect of age (e.g. so that the difference between the first and third quintile need not be twice the difference of the first and second) but this approach leads to residual confounding and in some cases -- like using deciles -- spends degrees of freedom needlessly.
Instead, authors should use splines as recommended by Frank Harrell in his book Regression Modelling Strategies.
