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st: Hotdeck imputation


From   "Daniel Waxman" <dan@amplecat.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Hotdeck imputation
Date   Sat, 11 Jun 2005 17:18:13 -0400

I need to do a relatively simple imputation, but am having trouble following
the examples given.  
Here is the situation:

Dataset ~ 10,000 obs (non-weighted, 1 obs/subject)

Variable to be imputed:
 EKG_abnormal     --binary(yes/no),  missing at random < 5% of observations.

Potential predictors with which to impute:  
At least five, some binary (e.g. chestpain yes/no, first_cat (1-5), etc.)
some which are continuous but can be made categorical (e.g. age ==> age_cat)

Primary outcome being studied:  Death yes/no

The questions:
(1) Should I use the outcome variable (death) as one of imputation
variables?  Should I use many imputation variables since I can (large
dataset?_

(2) Most important:  Can somebody give an example for the correct way to
issue the commands?

If I do the following:

. hotdeck ekg_abnormal using imp, by(agecat first_cat) store
keep(merge_variable) impute(5)

Then I end up with 5 files, imp1 imp2 imp3 imp4 imp5
Eventually I want to end up with imputed values for ekg_abnormal that I can
use the main logistic regression equation of interest.  Not sure where the
options infile(), command(logit) fit into things.

Any help would be greatly appreciated.




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