# Why not always use generalized estimating equations (GEE) instead of linear mixed models?

I read about generalized estimating equations (GEE) here, here and at other sites.

It is mentioned in first of above links that “the parameter estimates are nearly identical” for linear models but not for non-linear models.

In most situations, we are not able to predict if the relation will be linear. Then, why not perform GEE all the time rather than linear mixed method?

I think there could be some confusion caused by those links. I believe the statement about “not for nonlinear models” is actually referring to generalised linear mixed models (GLMMs), for example when the response is binary or a count or generally whenever a non-gaussian link function is used; and not a nonlinear mixed model, such as those that can be fitted with nlme like the logistic growth model $$f(x)={\frac {L}{1+e^{-k(x-x_{0})}}}$$ where we would no longer have a linear predictor. GLMMs still have a linear predictor, but a lot of the literature on GLMMs talks about them being nonlinear models due to the link function, but not the functional form of the model iteslf. This inevitably can lead to some confusion.
So there is indeed an argument for the use of GEE rather than GLMM when the marginal (population averaged) interpretation is wanted. GEEs are also useful when the correlation structure is mispecified, as the standard errors are robust. On the other hand GEE is know to require larger sample sizes and is not robust to data missing at random whereas GLMMs generally are. Finally, the GLMMAdaptive package in R can produced marginal as well as conditional estimates.