I'm running logistic regression for outcome on SBP (systolic blood pressure), DBP (diastolic blood pressure), BMI, and other covariates. Because SBP, BMI didn't show linear relationship with logit of outcome, I changed these continuous variables to categorical variable by quintile. Below are the models I tried:
Model1 : Logistic outcome covariates BMI SBP Model2 : Logistic outcome covariates BMI DBP Model3 : Logistic outcome covariates BMI SBP DBP Model4 : Logistic outcome covariates BMI SBP DBP SBP*DBP
This is model 1, model 2
This is model 3
Because in model 3, there was a significance change in 2nd 3rd of SBP and 3rd 4th of DBP compared with model 1, model2, I guess 'Is there any collinearity between SBP and DBP?' But all VIF values were below 3.
So then I would like to check interaction between SBP and DBP. I made an interaction term using categorical SBP and DBP, it resulted in 16 level variable. This is model 4:
All levels of interaction term were not significant, but SBP and DBP lost its significance in most of the levels.
I have some question about my analysis:
- Do I have to include interaction term into the model or not? Do you think is there any collinearity or interaction between SBP and DBP?
- How can I interpret difference between model 1, and 2 vs. model 3.
- Can only small VIF value confirm absence of multicollinearity? I ask this question cause condition index was 88.68. How low VIF and high condition index are present same time?