# Conditional vs. Marginal models

I have data with an outcome of 0 or 1 (binary) representing success or failure. I also have two comparison groups (Treatment vs. Control). Each subject in the study contributed 2 observations (the treatment is ear drops, so 2 ears). I wanted to model the data and to look for differences between treatment and control. I ran both a generalized linear mixed model (PROC GLIMMIX in SAS) which is a conditional model, and a GEE (PROC GENMOD in SAS), which is marginal. I got very similar estimations of the outcome probabilities in the two groups, and also similar p values. My question is, what is the difference between the marginal and conditional model, in general and in the context of this problem, and how do I know which one to choose and when ?

Marginal models are population-average models whereas conditional models are subject-specific. As a result, there are subtle differences in interpretation. For example if you were studying the effect of BMI on blood pressure and you were using marginal model, you would say something like, "a 1 unit increase in BMI is associated with a $Z$-unit average increase in blood pressure" while with a conditional model you would say something like "a 1 unit increase in BMI is associated with a $Z$-unit average increase in blood pressure, holding each random effect for individual constant."