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RE: st: mi impute chained

From   chong shiauyun <>
To   <>
Subject   RE: st: mi impute chained
Date   Fri, 2 Nov 2012 16:28:28 +0800

I used -force- in my MI model it resulted in a number of missing values which can't be imputed. For example, I have 7000 missing data on IQ and only 1800 are imputed. 

I know this is because of the missingness of predictors in my model but I don't understand why is happens because I have already specified to use -mi impute chained- to impute other predictors as well.

is there any ways to overcome this problem?

This is how the IQ conditional model looks like:

truncreg totaliq birthweight i.smkpreg i.marist i.matdepr i.homeown3 i.alcpreg3 i.hhcrowd i.ednmatpat i.findiff i.ethnicity i.scmatpat3 i.mumhealth i.breastfed verbiq perfiq mumiq tempcatwlc_i tempcatclc_i sex mumage babygestation , ll(45) ul(151) noisily

Many thanks


> Date: Wed, 31 Oct 2012 08:48:39 -0400
> Subject: Re: st: mi impute chained
> From:
> To:
> On Wed, Oct 31, 2012 at 4:54 AM, chong shiauyun <> wrote:
> > Hi,
> >
> > thanks for your advice.
> > I simplified my MI model by excluding some interactions and reduced some of my variables. It works fine. However, I am concern that I have to use the -force- option  to make the model works. It am quiet reluctant to drop all of the interactions seeing that it may affect the relationship between the exposure and the outcome which I am interested in.>
> I've used -force- and I think it works OK but check using
> -midiagplots-, which you can download.
> As to the interactions, if they're very collinear with the other
> variables you have in the model they're not adding anything. You can
> experiment with dropping or adding variables and keep checking with
> -midiagplots- to determine how things are working. Remember, the idea
> in MI is not to have a perfectly reconstructed dataset but to
> optimally preserve insofar as is possible the information you do have.
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