Mixed model vs GEE
Dimitris Rizopoulos covers this in his course slides: http://www.drizopoulos.com/courses/EMC/CE08.pdf The slides might be a bit math heavy for end users, but big important assumptions and intuitions are called out in clear language.
On 4/11/21 7:38 pm, Ben Bolker wrote:
?? I think that depends on what kind of questions you are asking ... ?? (If anyone wants to point to a great resource on marginal vs conditional models and when each type is appropriate, that would be great.? I know this distinction is discussed in Agresti's _Categorical Data Analysis_ book but I don't know if it goes into detail / gives examples about when one would want either one ...) On 11/4/21 8:22 PM, Tahsin Ferdous wrote:
Hi all, I am analyzing repeated measures data. Both the mixed model and generalized estimating equation are appropriate for my data. In this case, how can I decide that which one is better (LMM or GEE)? I know that GEE is a *marginal model*. It seeks to model a population average. Mixed-effect/Multilevel models are *subject-specific*, or *conditional*, models. Thanks. Best, Tahsin ????[[alternative HTML version deleted]]
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