One useful reference is :
Muff S, Held L, Keller L (2016) Marginal or conditional regression models
for correlated non-normal data? Methods in Ecology and Evolution 7:
1514?1524. doi: 10.1111/2041-210X.12623
Milani
On 5 Nov 2021, at 10:38 am, Ben Bolker <bbolker at gmail.com> 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
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
model*. It seeks to model a population average. Mixed-effect/Multilevel
models are *subject-specific*, or *conditional*, models. Thanks.
Best,
Tahsin
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