I'm conducting a study on how pregnancy weight gain affects risk of breast cancer and decided to go with a logistic regression model (outcome is yes/no for breast cancer) and my primary independent variable is categorical (<10lbs, 10-19 lbs, 30-39 lbs and >40 lbs each compared to the referent 20-29lbs). I've recently been told that mixed modeling may be a better alternative to account for random effects (which as I understand is basically variation between subjects if I treat that as a random effect for example).
My question is: are there any major drawbacks to using mixed-effects logistic regression? Is it a more complex model by any chance that I may not necessarily need? Could it inflate odds ratios?
In other words, how do I defend my use of logistic regression over mixed-effects logistic regression, if I can?