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st: R: MICE-Imputation: dealing with "plausible missings" and multilevel data


From   "Carlo Lazzaro" <carlo.lazzaro@tin.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: R: MICE-Imputation: dealing with "plausible missings" and multilevel data
Date   Fri, 19 Sep 2008 12:08:34 +0200

Dear Cornelia,
hoping to point out a useful reference on dealing with missing data, please
find below the following one:

Briggs A, Clark T, Wolstenholme J and Clarke P. Missing....presumed at
random: cost-analysis of incomplete data. Health Economics 2003; 12: 377–392

Kind Regards,

Carlo
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Gresch,Cornelia
Inviato: venerd́ 19 settembre 2008 10.10
A: statalist@hsphsun2.harvard.edu
Oggetto: st: MICE-Imputation: dealing with "plausible missings" and
multilevel data

Hi all,


currently I'm occupied by implementing an imputation-model for a large
dataset using Multiple imputation by the MICE system of chained
equations (ado -ice-).

Here I have two questions: 

First I would like to know how to deal with missing values which should
neither be imputed themselves nor serve (with misleadingly imputed
values) as predictors for other variables. 

E.g.: We have some questions which only should be answered by
respondents with migration background on which we apply a filter for
further tracing (e.g. if they are motivated to go back to their original
country). In such a case it makes only sense to impute values for
respondents with migration background. This is less problematic since
the meaningless values can just be deleted after the imputation process.

However, while running MICE, the variable with the misleadingly imputed
values is also used to impute other variables. And this definitely
doesn't make sense. Is there anybody of you who also has dealt with this
problem and found a handy solution for it?

E.g. is there any way to use a conditional "passive option" to replace
all values = 99 in case the preceding filter variable has a specific
value? The only (not convincing) solution I would see was to exclude the
corresponding variables with "plausible missings" as predictors for all
other variables (which could be done by using the eqlist-option).


Furthermore, we have multilevel-data (individual-level and school-level)
- is there any smart way to integrate this upper level into the
imputation-procedure?


Thanks in advance for any response/suggestion/support
Cornelia


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