So which one do you use in your statistical models? Sometimes, in diagnosis based data sets, you don't have a choice (Hypertension is a diagnosis but blood pressure may not be captured).
It seems like a simple question but it includes a lot of complexity. The binary variable is well understood, known to be a relevant change in patient characteristics and can account for things like medication treatment. The continuous variable, whule it has a lot more information, needs some assumptions on spacification. For example, can we really assume linearity of an association between blood pressure and a clinical outcome? If we only have treated blood pressure is that the parameter of interest or is it the "underlying level of blood pressure"? If the later, we have a messy missing data problem.
I admit, as a statistics guy, I strongly incline towards the continuous version of the variable. But it is not at all clear to me that it is always the dominant choice for dealing with these types of varibles.
In which I side with Neyman over Fisher
1 hour ago