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Re: st: Missing outcome variables - how to deal with these?


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Missing outcome variables - how to deal with these?
Date   Fri, 22 May 2009 09:52:08 +0000 (GMT)

--- On Thu, 21/5/09, Tomas M wrote:
> I have a set of data, with missing outcome variables.  
> I plan to use multinomial logistic regression to
> analyze the association of my predictor variables with
> the outcomes. <snip> How would I deal with these missing
> outcome values in Stata?
> 
> Would I drop the missing values from analyses?  Or
> would I include them as a separate outcome in my multinomial
> logistic regression models?
> 
> Would it also be possible to impute the missing outcome
> values using some function to predict the outcome based on a
> given set of independent predictor variables (derived based
> on the set of full and complete data)?  If so, how
> would I go about this in Stata?

Most of the remidies for this type of problem assume that the
probability of having a missing value only depedends on 
observed characteristics of the respondent (this is the so-
called Missing At Ramdom or MAR assumption)

If you have only missing observations on the dependent variable
then just leaving the missing observations out of the analysis
will lead to unbiased estimates whenever the remedies are 
appropriate (see for example Allison 2002, footnote 1). So, in 
these cases these remedies add very little, except that you use
information from more observations. However, this is only 
helpful if you have few observations to begin with.

So, in your case I would just declare those missing values as 
missing (=.) and do nothing else, unless you have a very small
sample size. The missing values will than be automatically 
ignored. If you want to do an imputation I would use Patrick 
Royston's (2004, 2005a, 2005b, 2007) -ice- command. 

Hope this helps,
Maarten

Paul D. Allison (2002) Missing Data, Quantitative Applications
in the Social Sciences, nr. 136. Thousand Oaks: Sage.

Royston, P. 2004. Multiple imputation of missing values. Stata Journal
4(3): 227--241.

Royston, P. 2005a. Multiple imputation of missing values: update. Stata
Journal 5(2): 188--201.

Royston, P. 2005b. Multiple imputation of missing values: Update of ice.
Stata Journal 5(4): 527--536. 

Royston, P. 2007. Multiple imputation of missing values: further update
of ice, with an emphasis on interval censoring. Stata Journal 7(4):
445--464.

-----------------------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://home.fsw.vu.nl/m.buis/
-----------------------------------------


      

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