I have a binary logistic regression model with a DV (disease: yes/no) and 5 predictors (demographics [age, gender, tobacco smoking (yes/no)], a medical index (ordinal) and one random treatment [yes/no]). I have also modeled all the two-sided interaction terms. The main variables are centered and there is no sign of multicollinearity (all VIFs < 2.5).
I have some questions:
Is bootstrapping advantageous over my single model? if so,
which bootstrapped model should I choose? I just wanted to see if bootstrapping algorithms follow random methods for creating new samples, or if they have rigid algorithms. Therefore, I resampled for 1000 times in each attempt (so I have several bootstrapped models, each with 1000 trials). However, each time the coefficients of the bootstrapped model differ (although the number of trials are constantly 1000). So I wonder which one should I choose for my report? Some changes are tiny and don't affect my coefficients' significance, but some make some of my coefficients non-significant (only those with P values close to 0.05 in the original model that change to 0.06 for example).
Should I choose a higher number like 10,000? How can I determine this limit?
Again should I bootstrap in the first place? If its results vary each time, can I rely on its results?
Do you have any other ideas in mind that can help me with my case?
Many many thanks.