Question :GEE with missing data

[Clare Rutterford-2]

Dear Allstat,

 

I have a longitudinal dataset from a 2 treatment group clinical trial with
baseline and 4 follow up timepoints. I may have missing baseline covariates
and follow up data.

 

As I am interested in the average effect of the treatment groups I wish to
analyse using a GEE model.  As the missing data is likely to be Missing At
Random (MAR) I have read that GEE models are likely to provide biased
estimates.

 

My thoughts were to multiply impute the missing data and then carry out a
GEE model.  

 

I would be interested to hear your views on whether this is the best
approach (if so how best to impute the missing data?) , or are there
alternative approaches.  

 

 

Best Wishes

 

Clare Rutterford

 

[EDIEBAH Divine Ewane]

Dear Clare,

 

 

The GEE results are valid only when there is a small and asymptotically diminishing fraction of missing data

or when the data are missing completely at random (MCAR). Correctly specification of

the correlation struture is required to obtain consistent result for the parameter estimates and their variances

when the patttern of the missing data at a particular time point depends on the previous outcomes.

Therefore, the robustness to the choice of the correlation structure does not remain valid in the case of nonrandom missing data.

 

If the fraction of missingness is quite small , GEE models will provide valide estimates.

Or if your data are normal why not go for Mixed model which assumes MAR?

 

 

Regards,

 

Divine
 

> Date: Tue, 3 Nov 2009 12:29:23 +0000
> From: c.m.rutterford@...
> Subject: Question :GEE with missing data
> To: allstat@...
>
> Dear Allstat,
>
>
>
> I have a longitudinal dataset from a 2 treatment group clinical trial with
> baseline and 4 follow up timepoints. I may have missing baseline covariates
> and follow up data.
>
>
>
> As I am interested in the average effect of the treatment groups I wish to
> analyse using a GEE model. As the missing data is likely to be Missing At
> Random (MAR) I have read that GEE models are likely to provide biased
> estimates.
>
>
>
> My thoughts were to multiply impute the missing data and then carry out a
> GEE model.
>
>
>
> I would be interested to hear your views on whether this is the best
> approach (if so how best to impute the missing data?) , or are there
> alternative approaches.
>
>
>
>
>
> Best Wishes
>
>
>
> Clare Rutterford
>
>

     
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