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Re: st: Missing outcome variables - how to deal with these? OR moels and reality


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Missing outcome variables - how to deal with these? OR moels and reality
Date   Mon, 25 May 2009 07:43:58 +0000 (GMT)

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

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


--- On Sun, 24/5/09, jmpsouza wrote:
> may I present a situation very usual, indeed one that
> already happened to me, a possible way to handle it and the
> question whether the conduct is reasonable:
> 1. the data come from official registry;
> 2. the outcome is birth weigth, coded 0 for normal and 1
> for low, no missing;
> 3. the  model is logistic (just for simplify, since it
> was used glm poisson robust as an estimate of the prevalence
> ratios);
> 4. var1, var2, var3, var4 with very very few missings
> (let´s say less than 2%);
> 5. var5, with 4 categories, but with about 20% missing
> answers;
> 6. var6, same as var5;
> 7. var5 received then 5 categories: 1 , 2 , 3 , 4, 
> the same original ones and 5, when missing;
> 8. tab(var5), gen(var5);
> 9. same for var6;
> 10. any of var51 or var52 or var53 or var54 is used as
> reference, the others, including var55 are in the command ;
> 11. same for var 61, var62 etc.
> 12. the command is logistic weigth var1 var2 var3
> var4  var52 var53 var54 var55 var62 var63 var64 var65;
> 13. in the analysis var55 and var65 are placeholders, but
> their odds ratios may be useful.
> The question: may this be used, is it too difficult to
> accept? Is it a situation for using -ice-?

The problem with this method is explained here:

http://www.stata.com/statalist/archive/2007-12/msg00030.html

Hope this helps,
Maarten



      

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