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RE: st: Question about multinomial logistic regression and random effects with multiply imputed data


From   "Barksdale, Crystal" <cbarksda@jhsph.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Question about multinomial logistic regression and random effects with multiply imputed data
Date   Thu, 31 Jul 2008 09:50:51 -0400

Thank you Maarten, for your assistance and suggestions.  Unfortunately,
our group has already explored using the Stata program gllamm with our
multiply imputed data, and it does not work with the "mim" commands.  

We are considering running the analyses on each multiply imputed dataset
(separately), and then trying to combine the results manually; however,
we are having some difficulty figuring out how to store the parameter
estimates and standard errors for each variable and it's corresponding
category.  For example, if I run a multinomial logistic regression with
an outcome variable with 3 categories, I will get three separate
estimates of the predictor.  It is not immediately clear how to save
these three separate estimates and standard errors, to use ultimately in
combining the estimates across the multiply imputed datasets. Any
additional thoughts or suggestions are welcome.

Thank you,
Crystal 

Crystal L. Barksdale, Ph.D.
Postdoctoral Fellow
Johns Hopkins Bloomberg School of Public Health
624 N. Broadway
Hampton House, 808
Baltimore, MD 21205
(410) 502-9344 (office)
(410) 955-9088 (fax)
-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis
Sent: Tuesday, July 29, 2008 6:04 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Question about multinomial logistic regression and
random effects with multiply imputed data

--- Crystal Barksdale <cbarksda@jhsph.edu> wrote:
> I am wondering how (and if) I can do a multinomial random effects
> logistic regression with multiply imputed data. 

Multinomial random effects logistic regression is discussed in (Haan &
Uhlendorf 2006). A nice point of Multiple Imputation is that it is not
method-specific, all it requires is that the sampling distribution of
the parameters is (approximately) Gaussian / normal. As long as this is
(approximately) true, you can use Multiple Imputation for your
multinomial random effects logistic regression. 

There are ofcourse other assumptions like the missing data needs to be
MAR, and if you don't know what that means you'll have to start reading
before you touch -ice- or -mim-. A good starting point is (Allison
2001).

-- Maarten

Allison, P. (2001) Missing Data, Thousand Oaks: Sage.

Haan, P. and Uhlendorf, A. (2006) Estimation of multinomial logit
models with unobserved heterogeneity using maximum simulated
likelihood. The Stata Journal, 6(2): 229--245.

http://www.stata-journal.com/article.html?article=st0104

-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

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


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