Re: st: ICE interpretation

["Michael I. Lichter" <mlichter@buffalo.edu>]

It sounds like you may be on the right track. How many cases were 
missing data on i.gene1 that were non-missing on response1? Another way 
of putting it: what was the n for the logit on your original data, and 
what was the n for the data after ice?

-ml

Sham Lal wrote:
> Dear all, 
>
> I'm currently having some issues with the interpretation of an imputed dataset. The study aims to identify gene associated with response to drug treatment. I imputed a series of clinical response variables in the imputation equation, to gain what I would expect to be more robust estimates. I did not impute the gene's. 
>
> Upon using mim and performing various logistic regressions, using for example, 
> xi: mim: logit response1 i.gene1
>
> using this imputed dataset gives me a nearly identical estimates (ORs, SE, 95%ci) as my unimputed dataset (changes occurred 4th decimal place). So in fact i've not gained anything from using an imputed dataset. Could this be because not imputed by predictor variable, the gene?
>
> Any thoughts appreciated
> Regards, 
> Sham 
>
>
>
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--
Michael I. Lichter, Ph.D.
Research Assistant Professor & NRSA Fellow
UB Department of Family Medicine / Primary Care Research Institute
UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
Office: CC 125 / Phone: 716-898-4751 / E-Mail: mlichter@buffalo.edu
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