[R-sig-ME] Mixed model vs GEE

[Tahsin Ferdous]

Dear all,

Thanks a lot.

Best,

Tahsin

On Thu, Nov 4, 2021 at 10:02 PM Milani Chaloupka <m.chaloupka using uq.edu.au>
wrote:

> 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 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.
> >> Best,
> >> Tahsin
> >>      [[alternative HTML version deleted]]
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