An HTML attachment was scrubbed... URL: http://lists.utsouthwestern.edu/pipermail/impute/attachments/20080101/f508cc24/attachment.htm From zaslavsk <@t> hcp.med.harvard.edu Wed Jan 2 09:07:14 2008 From: zaslavsk <@t> hcp.med.harvard.edu (Alan Zaslavsky) Date: Wed Jan 2 09:07:37 2008 Subject: [Impute] Rounding option on PROC MI and choosing a final MI dataset Message-ID: <[email protected]>
> From: "Raquel Hampton" <[email protected]> > Subject: [Impute] Rounding option on PROC MI and choosing a final MI > dataset > My first question is: there is a round option for PROC MI, but I read in > an article (Horton, N.J., Lipsitz, S.P., & Parzen, M. (2003). A > potential for bias when rounding in multiple imputation. The American > Statistician 57(4), 229-232) that using the round option for categorical > data (the items have nominal responses, ranging from 1 to 5) produces > bias estimates, though logical. So what can be done? I only have access > to SAS and STATA, but I am not very familar with STATA. Will this not > be such a problem since the proportion of missing for each individual > item is small? Do you really mean nominal (unordered categories, like French, German, English, or chocolate, vanilla, strawberry) or ordinal (like poor, fair, good, excellent)? If nominal, you won't get anything sensible by fitting a normal model and rounding. If ordinal and well distributed across the categories, the bias of using rounded data will be less than with the binomial data primarily considered by the Horton et al. article. You might also consider whether it is necessary to round at all -- depends on how the data will be used in further analyses. With only a couple of percent missing on each item, all of the issues about imputation become less crucial, although as noted in a previous response you should definitely run the proper MI analysis to verify that the between-imputation contribution to variance is small. In practice any modeling exercise is a compromise involving putting more effort into the important aspects of the modeling and in this case this might not require doing the most methodologically advanced things with the imputation.
