[R-sig-ME] Mixed model vs GEE
p@u|@john@on @end|ng |rom g|@@gow@@c@uk
Fri Nov 5 12:51:54 CET 2021
This might be useful, although it's focussed on non-normal GLMMs, not LMMs. I read it a while ago, but I remember it being excellent:
Marginal or conditional regression models for correlated non-normal data?
Stefanie Muff, Leonhard Held, Lukas F. Keller
"For normally distributed response variables, that is in linear regression, the choice between a marginal and a conditional formulation is not particularly delicate, because the interpretation of conditional and marginal linear regression models turns out to be equivalent. On the other hand, the choice is relevant for non-normal data, as the interpretation of conditional and marginal regression models is usually different."
On 05/11/2021, 00:38, "R-sig-mixed-models on behalf of Ben Bolker" <r-sig-mixed-models-bounces using r-project.org on behalf of bbolker using 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 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.
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