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?
Best Answer
There are different approaches of including random effects in your model (random slope model, random slope + intercept model, random intercept model). You can check for the magnitude of random effects using for example "induced correlation" (Zuur et al. 2008).
Based on its magnitude you can then justify whether to include the random effect in your model or not.
While there is no threshold (at least that I know of) at which to include or exclude a random effect in your model (talking about "induced correlation"), you can most often easily justify the exclusion of random effects with a value close to 0. Furthermore, you have a generalized comparison of the influences of your random effects.
In general, simple models are preferred over complex ones in statistical modelling.