[R-sig-ME] Mixed model vs GEE
me @end|ng |rom ph||||p@|d@y@com
Fri Nov 5 14:22:11 CET 2021
Dimitris Rizopoulos covers this in his course slides:
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
>> 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.
>> [[alternative HTML version deleted]]
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