[R-sig-ME] Mixed model vs GEE
Carl Von Ende
cvonende @end|ng |rom n|u@edu
Sat Nov 6 23:34:02 CET 2021
Two Wiley references that specifically GEE might be useful. They both are listed in the References of the Muff et al. (2016) paper cited below in Message 1.
1. Applied Longitudinal Analysis, 2nd Edition, 2011
by Garrett Fitzmaurice, Nan Laird & James Ware'
Then spend considerable time comparing LMM vs GEE
https://content.sph.harvard.edu/fitzmaur/ala2e/
The software used primarily is SAS, but there are a few R programs on the above website.
2. Generalized Estimating Equations, 2nd Edition, 2013
The authors use the R packages geepack, gee, and vags, as well Stata.
On 11/5/21, 9:31 AM, "R-sig-mixed-models on behalf of r-sig-mixed-models-request using r-project.org" <r-sig-mixed-models-bounces using r-project.org on behalf of r-sig-mixed-models-request using r-project.org> wrote:
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Today's Topics:
1. Re: Mixed model vs GEE (Paul Johnson)
2. Re: Mixed model vs GEE (Phillip Alday)
3. Re: Mixed model for unbalanced data (Phillip Alday)
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Message: 1
Date: Fri, 5 Nov 2021 11:51:54 +0000
From: Paul Johnson <paul.johnson using glasgow.ac.uk>
To: Ben Bolker <bbolker using gmail.com>, "r-sig-mixed-models using r-project.org"
<r-sig-mixed-models using r-project.org>
Subject: Re: [R-sig-ME] Mixed model vs GEE
Message-ID: <DE210BC8-89AF-45D8-8AE1-8C58AEBC6DF0 using glasgow.ac.uk>
Content-Type: text/plain; charset="utf-8"
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
https://doi.org/10.1111/2041-210X.12623
"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.
>
>
> Best,
>
> Tahsin
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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Message: 2
Date: Fri, 5 Nov 2021 08:22:11 -0500
From: Phillip Alday <me using phillipalday.com>
To: Ben Bolker <bbolker using gmail.com>, r-sig-mixed-models using r-project.org
Subject: Re: [R-sig-ME] Mixed model vs GEE
Message-ID: <9067f07b-5ba5-9346-0f0b-3b340a821a34 using phillipalday.com>
Content-Type: text/plain; charset="utf-8"
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]]
>>
>> _______________________________________________
>> R-sig-mixed-models using r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
------------------------------
Message: 3
Date: Fri, 5 Nov 2021 09:20:43 -0500
From: Phillip Alday <me using phillipalday.com>
To: Tahsin Ferdous <tahsinferdousuofc using gmail.com>,
r-sig-mixed-models using r-project.org, Ben Bolker <bbolker using gmail.com>
Subject: Re: [R-sig-ME] Mixed model for unbalanced data
Message-ID: <434e4c2a-c4c4-1dc6-1392-88b4b4cd92e5 using phillipalday.com>
Content-Type: text/plain; charset="utf-8"
Yes.
There will of course be greater uncertainty for conditions/groups with
less data.
The best way to see how this impacts your inference is to simulate
balanced and unbalanced data for your hypothesis and look at the
difference in estimates, standard errors, etc.
On 5/11/21 12:10 am, Tahsin Ferdous wrote:
> Dear all,
>
> Can we use unbalanced data for the mixed model with nested random effects?
>
> Best,
>
> Tahsin
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
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