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st: RE: RE: Imputing values for categorical data


From   "Dupont, William" <william.dupont@vanderbilt.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: Imputing values for categorical data
Date   Thu, 15 Apr 2004 15:07:23 -0500

Nick

Your imputation is correct.  Thanks for the clarification.

Bill

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
Sent: Thursday, April 15, 2004 3:03 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: RE: Imputing values for categorical data


impute the missing reference here. In this case, it 
happens to be a book I know about. 
(In other cases, in other postings, just giving 
author surnames and dates makes the reference search 
difficult: list members please note.) 

Statistical Analysis With Missing Data, Second Edition
Roderick J. A. Little, Donald B. Rubin
ISBN: 0-471-18386-5 Wiley 
September 2002 

Nick 
n.j.cox@durham.ac.uk 

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Dupont, 
> William
> Sent: 15 April 2004 20:47
> To: statalist@hsphsun2.harvard.edu
> Subject: st: RE: Imputing values for categorical data
> 
> 
> Jennifer
> 
> In my opinion, imputation makes the most sense when we wish to adjust 
> for confounding variables.  Suppose that I am primarily interested in 
> the relationship between y and x, and I have complete data on these 
> two variables from my data set.  I feel, however, that I should adjust

> my analysis for a number of other confounding covariates and I know 
> that missing values are scattered throughout these covariates.  If I 
> just regress y against x and these other covariates I get a complete 
> case
> analysis: any record that is missing any value of these covariates is 
> dropped from the analysis.  This can lead to a substantial loss of 
> power and has the potential to induce bias if having complete data
> is related
> to the response of interest.  Suppose that one of my confounding
> variables is gender.  If I have a number of records where y and x are
> known but gender is not, it does not seem sensible to throw out this
> information just because I would like to adjust my estimates 
> for gender.
> If, however, I impute gender I can avoid loosing these data.  
> As long as
> gender is only in the model as a confounder, I don't see that it does
> much harm to have an imputed value of say .2 for some patient, which
> means that based on her other covariates that she is 5 times 
> more likely
> to be of one gender than the other.
> 
> A tricky problem with imputation is that we often lack assurance that 
> the missing values are missing at random.  However, even in this 
> situation, it is unclear that the complete case analysis is superior 
> to an imputed analysis for the situation described above.  Imputation
> becomes much more problematic when some variables of primary interest
> have missing values.
> 
> The imputation gurus do not like the single conditional imputation 
> provided by Stata (see for example Little and Rubin 2002).  This is 
> because this technique underestimates the standard error of the 
> regression coefficient for covariates with imputed values and 
> overestimates the degrees of freedom.  Multiple imputation methods get

> around this problem and are fine as long as you are confident that the

> missing values are missing at random.  If your are only using 
> imputation for confounding variables I'm not convinced that it makes 
> much difference how you do the imputation.  However, multiple 
> imputation is always theoretically preferable and can avoid hassles in

> the event that
> you come up against a referee who objects to all use of single
> conditional imputation.

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