Message-ID: <9067f07b-5ba5-9346-0f0b-3b340a821a34@phillipalday.com>
Date: 2021-11-05T13:22:11Z
From: Phillip Alday
Subject: Mixed model vs GEE
In-Reply-To: <739dc508-6a34-23e9-2f89-de0c926a4e51@gmail.com>
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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>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
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