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

From   "Rodrigo Alfaro A." <[email protected]>
To   <[email protected]>
Subject   st: RE: R: MICE-Imputation: dealing with "plausible missings" and multilevel data
Date   Mon, 22 Sep 2008 13:10:58 -0400



We have a similar problem but in a different setting: level, and groups.

(1) Level: It seems that you need to define at what level you want to do the imputation. In our case, we are working with household surveys then we have 2 levels of information: individuals and households. Our goal is to have an analysis of debt/income/asset then household level is the one we choose. Given our experience in the last Stata meeting at UK we conclude that we need to do some conditional imputation at the individual level, such as Hot-Deck or maybe some non-proper imputation method. With that in hand we could add that information at the next level (households). For example, missing observations in labor income could be "solved" by some previous conditional imputation in order to provide some estimate for the total household income, which will be the sum of individual ones.

(2) Groups: We conclude that conditions on variables define groups of observations. Again, in our household survey we have cases with and without bank loans. In this case we consider 2 groups to do the imputation. It should be noted that: we cannot impute some amount of bank loans for households without it, and also we cannot force that without bank loans implies zero bank loans. In our case, those households do not have that kind of loans for a specific reason: they do not have access to them. As you see creating groups implies a huge task in the sense that we need to specific some particular kind of household in the combination of many variables. However, this makes more sense than imputing any value and then putting the true missing back. 

I hope this helps you.
Best regards, Rodrigo. 



-----Mensaje original-----
De: [email protected] [mailto:[email protected]] En nombre de Carlo Lazzaro
Enviado el: Viernes, 19 de Septiembre de 2008 06:09 a.m.
Para: [email protected]
CC: 'Gresch,Cornelia'
Asunto: st: R: MICE-Imputation: dealing with "plausible missings" and multilevel data

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,

-----Messaggio originale-----
Da: [email protected]
[mailto:[email protected]] Per conto di Gresch,Cornelia
Inviato: venerd� 19 settembre 2008 10.10
A: [email protected]
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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