MAR or NMAR in negative binoial model PreviousNext
Mplus Discussion > Missing Data Modeling >
 Jamie R. Johnson posted on Saturday, February 17, 2018 - 8:55 am
I am trying to make sure the results of an analysis I ran are robust to the missing data properties. The missingness is either MAR or NMAR.

There is 1 exogenous predictor variable and 4 DVs.

The 4 DVs are also count data (negative binomial) which the
command says cannot be implemented with count data.

Is there an acceptable way to do this? I have implemented the MLR estimator to account for the data distribution with the AUXILLARY command with the variables coded for missingness but am unsure if there is a better method.

The missingness is most likely related to the response variables and is also a function of the predictor variable (more missingness in 1 group than the other)

Thank you!
 Bengt O. Muthen posted on Saturday, February 17, 2018 - 2:05 pm
It sounds like you want Aux=(M) done manually for counts. I don't know of an easy way to do that because we don't have WITH for counts. You say that the DV values might predict missingness on them. But I don't know how to do Diggle-Kenward type missing data modeling in this situation. The best you can do is probably stay with MLR, that is, act as if MAR holds.
 Jamie R. Johnson posted on Tuesday, February 20, 2018 - 4:48 am
Thank you. I noticed the WITH statement wasn't allowed as well with counts. So to confirm, stick with the MLR and use the AUXILLARY = (M) code or were you suggesting dropping the auxillary part as well?
 Bengt O. Muthen posted on Tuesday, February 20, 2018 - 2:38 pm
I don't think Aux=(M) is implemented for counts for the reason related to WITH. So just do MLR.
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