<s>[INST] This problem is related to Generalized Linear Mixed Model (GLMM) and Generalized Estimating Equations (GEE). I will first give a brief overview of the methods and then ask my specific problem.

**Background**
GLMM is a statistical regression model for data that are clustered or have some form of non-independence in the responses, allowing different residual structures. In addition to the usual fixed effects, GLMM includes random effects in the linear predictor, which allows the response to vary by each group or cluster.

GEE is an extension of Generalized Linear Model (GLM) that deals with correlated data. Similar to GLMM, GEE is used to estimate the effect of independent variables on a dependent variable in clustered data. Unlike GLMM which specifies a distribution for the random effects, GEE does not.

I have two independent variables, **x** and **y**, and one dependent variable, **z**. All variables are continuous. **x** and **y** are measured at the individual level, but **z** is clustered at the group level. In this case, **z** is the average of multiple measurements within each group.

Now to my question:
**Problem**: I am struggling with the decision between GLMM and GEE for my analysis. My data has a dependent variable which is clustered (average of multiple measurements), but I do not have any random effects in mind that could explain the variation between clusters, and I am not interested in estimating the variation between clusters. I just want to identify the relationships between **x**, **y**, and **z**.

Given my scenario, could someone help me choose between GLMM and GEE?[/INST]