Hi all,

I am working on the large data set on Major depression disorder. One of the 
outcome variable of interest is the Hamilton Depression Rating Scale(its a17 
item scale). About 28% of the exit data are missing. I would like to impute the 
missing data for the outcome varaible. There are several covariates associated 
with the outcome of the data among which one variable is highly correlated with 
the outcome variable. What is the correct way of modeling this kind of data and 
later for imputation?.

Thanks in advance for any help and suggestion on this question.

Bala
University of Pittsburgh

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From Howells_W <@t> bmc.wustl.edu  Thu Sep 30 10:15:10 2004
From: Howells_W <@t> bmc.wustl.edu (Howells, William)
Date: Sun Jun 26 08:25:02 2005
Subject: [Impute] Modeling and Imputation for MNAR data set
Message-ID: <2ada428b6944da4b8f8a2fdf4e60e52a01f...@exchange.wusm-pcf.wustl.edu>

I don't know the correct way of modeling as far as imputation model, but
I analyze similar data, and one issue that arose was whether to impute
the sum of the items or whether to impute the individual items and then
sum them up after imputation.  We chose the latter.  Bill Howells, Wash
U, St Louis

 

-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of
G.K.Balasubramani
Sent: Thursday, September 30, 2004 9:11 AM
To: [email protected]
Subject: [Impute] Modeling and Imputation for MNAR data set

 

Hi all,

 

I am working on the large data set on Major depression disorder. One of
the outcome variable of interest is the Hamilton Depression Rating
Scale(its a17 item scale). About 28% of the exit data are missing. I
would like to impute the missing data for the outcome varaible. There
are several covariates associated with the outcome of the data among
which one variable is highly correlated with the outcome variable. What
is the correct way of modeling this kind of data and later for
imputation?.

 

Thanks in advance for any help and suggestion on this question.

 

Bala

University of Pittsburgh

 

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