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st: R: Missing data analysis


From   "Carlo Lazzaro" <carlo.lazzaro@tin.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: R: Missing data analysis
Date   Mon, 1 Mar 2010 07:58:39 +0100

Dear Rosie,
I am not clear about what you mean with "we have to to delete cases that
have missing values", since this is not the standard practice.

If you mean (right)censored observations, they can be addressed in Stata via
Survival Analysis suite (please, see -stset- and related stuff in Stata
9.2/SE).

For more details on dealing with missing observations, especially when
they're variables rather than outcomes, you might want to take a look at:

Little RJA, Rubin DB. Statistical analysis with missing data. Second
Edition. Hoboken, NJ: Wiley, 2002.

HTH and Kind Regards,

Carlo 

-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Rosie Chen
Inviato: domenica 28 febbraio 2010 21.31
A: statalist@hsphsun2.harvard.edu
Oggetto: st: Missing data analysis

Hi, dear listserv members,

   I have a question that is not specifically related to Stata, but would
like to have a try in here: 

   In most studies, we have to delete cases that have missing values on the
outcome variable. The issue is whether the deleted cases are significantly
different from the final sample we use, because of the potential sample
selection bias problem.  My question is: do we often compare the deleted
cases with the final raw sample without missing data imputation or with the
final sample with missing cases imputed? Any suggestions are appreciated
very much,

  Rosie



      
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