I can't think of one. The sorts of estimates you get with a declared subpopulation are the same that you would get if you made your subpopulation variable a dimension in a table and then dropped the table elements outside of the subpopulation of interest. So it is like having imputed both a row variable and a response variable. I think WesVar will only allow multiply imputed response variables but if SUDAAN and Stata also allow multiply table variables, I am not sure why there would be a problem. Of course, if the two variables were not jointly imputed, then you may get bad point estimates, but that is a different issue.
--Dave From: Impute -- Imputations in Data Analysis [mailto:[email protected]] On Behalf Of Lucy Bilaver Sent: Wednesday, July 10, 2013 2:16 PM To: [email protected] Subject: Re: MI analysis with complex survey data Thanks. SUDAAN does allow multiply imputed variables in the subgroup statement. The regression will proceed without error or warning. My concern is that there is a statistical issue that I am overlooking. Stata allows users to specify a multiply imputed variable in a subpop statement also but requires an option (esampvaryok) after a big warning. -L On Wed, Jul 10, 2013 at 12:37 PM, David Judkins <[email protected]<mailto:[email protected]>> wrote: Users of SUDAAN need to be careful to always give it the full dataset and use the subgroup statement to get subpopulation estimates. Prior subsetting will not give correct results. I do not know though if SUDAAN allows you to have multiply imputed values for variables listed on the subgroup statement. I suspect not. This might be a case, where you need to write your own software. Easiest if you use replicated weights for the within-imputation variance. Here is one version of the SUDAAN warning: http://www.ats.ucla.edu/stat/sudaan/faq/subpopn.htm --Dave Judkins From: Impute -- Imputations in Data Analysis [mailto:[email protected]<mailto:[email protected]>] On Behalf Of Lucy Bilaver Sent: Wednesday, July 10, 2013 12:33 PM To: [email protected]<mailto:[email protected]> Subject: MI analysis with complex survey data Hi everyone, I have fit an imputation model for complex survey data that I am working with. I ran the imputation in Stata and have been doing the analysis in Stata and Sudaan. I am trying to understand what the issues are when analyzing a subsample of multiply imputed data that varies in size across imputations. The subsample is based on an imputed variable thus the reason for the slight variation across imputations is clear. Stata warns that this situation could "bias results" while Sudaan does not. Does anyone have thoughts on the implications of having estimation samples that vary across imputations when using a complex survey design? In both Stata and Sudaan, I am asking for SE to be computed using a Taylor linearization method. Thanks! -L -- Assistant Professor Northern Illinois University School of Nursing and Health Studies [email protected]<mailto:[email protected]> Affiliated Scholar Chapin Hall at the University of Chicago [email protected]<mailto:[email protected]> 773-256-5237<tel:773-256-5237> ________________________________ This message may contain privileged and confidential information intended solely for the addressee. Please do not read, disseminate or copy it unless you are the intended recipient. If this message has been received in error, we kindly ask that you notify the sender immediately by return email and delete all copies of the message from your system. -- Assistant Professor Northern Illinois University School of Nursing and Health Studies [email protected]<mailto:[email protected]> Affiliated Scholar Chapin Hall at the University of Chicago [email protected]<mailto:[email protected]> 773-256-5237 ________________________________ This message may contain privileged and confidential information intended solely for the addressee. Please do not read, disseminate or copy it unless you are the intended recipient. If this message has been received in error, we kindly ask that you notify the sender immediately by return email and delete all copies of the message from your system.
