I'm considering an example where I'm creating a model to predict a binary categorical outcome to determine whether a tool will be successful (e.g. 1,0) over different periods of time. If I create an independent variable that is a composite of other independent variables (e.g. ALT = Tot_age-TSLT) then can I also still use the independent variables that created it? For example, can I have a model that looks like this:
Outcome=beta_0 + beta_1(TSLT) + beta_2(ASLT)
I have been told that you should not use composite variables and the variables that make up the composite variable in the same model. Is there a concrete reason why and does this apply to every case?
- TSLT is time since last tested (The tool will be tested several times throughout its life in a simulated setting that is assumed to be as close to real life outcome as possible. Thus, if it is successful during simulated test, it is assumed it will be successful during real life. However, the further from the test date, the less certain of real life success you can be.)
- Tot_age is the age of the tool today based on its manufacture date
- ALT is age of the tool the last time it was tested