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


From   "Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   R: st: Missing outcome variables - how to deal with these?
Date   Sat, 23 May 2009 12:36:53 +0200

Dear Tomas,
just echoing Maarten's wise and at the same time discouraging remarks:
- even the reference textbook about this topic (Little RJA, Rubin DB.
Statistical analysis with missing data. 2nd edition. Chichester: Wiley:
2002) allots few pages to NMAR mechanism (from Subject Index: 12, 13-15,
18-19.
- Hence, the only possible way to deal with NMAR is to rely upon external
data sources for similar items (please, see: Ramsey S, Wilke R, Briggs A, et
al. Best practices for economic evaluations alongside clinical trials: an
ISPOR RCT_CEA Task Force report. Value Health 2005; 8: 521-33).
However, the problem seems to go out from the door and come back through the
window: it's again a matter of how good are those external sources for your
research needs. A possible further advice is to perform some sensitivity
analysis after filling in NMAR data and see what happens when you change
MNAR guess estimates within a reasonable or customarily range relative to
your research field.

Kind Regards and enjoy your W-E,
Carlo
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Maarten buis
Inviato: sabato 23 maggio 2009 10.03
A: statalist@hsphsun2.harvard.edu
Oggetto: RE: st: Missing outcome variables - how to deal with these?


--- On Fri, 22/5/09, Tomas M wrote:
> For my data, I am quite certain that the data is not
> missing at random (NMAR).  I have reason to believe
> that my missing outcome data is related to the outcome data
> itself.  I do have a full set of explanatory variables
> for all of my observations, however.
> 
> Does this mean that I cannot use the typical
> remedies?  What other options are there for analyzing
> missing data that is non-ignorable?

I have always stayed away from those NMAR models. The problem
is that they just can't produce empirical estimates: They 
critically depend on something that can't be seen. I realise
that there are questions out there that are so important that
we must just give the best "guesstimate" we can, even though
under normal circumstance that best guess would not be 
considered good enough. Till now I have been able to avoid
those questions, so I don't know the answer to your question.

-- Maarten

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

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


      

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