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# st: Re: multiple imputation and life tables

 From "Rodrigo A. Alfaro" To Subject st: Re: multiple imputation and life tables Date Fri, 23 Jun 2006 19:19:42 -0400

```Margaret,

Multiple Imputation (MI) allows you to deal with missing values.
The "solution" is that you will change your missing values for
several "possibles" values. For example if x has only one missing,
then MI creates 5 possible values for this missing, that's the
reason why you have N*5 number of observations.

Then you change your N-1 dataset (assuming only 1 missing
value) for N*5 dataset. The statistical support is based on
Bayesian ideas and you can google MI to learn more on that.

In your case you have to "run" the ltable command conditional
to the set imputed. If imp is the variable that describes the
# of imputation type

forvalues i=1/5 {
ltable...  if imp==`i', su h
}

Then your statistics (beg total, deaths, lost and rates) should
be averaged over the 5 tables. You should compute the
standard errors for these numbers and the formula is in
Joe Schafer's webpage: http://www.stat.psu.edu/~jls/mifaq.html
(see the question: How do I combine the results across the
multiply imputed sets of data?)

I hope this helps you
Rodrigo.

----- Original Message -----
From: "Margaret Gassanov" <gassanov.1@osu.edu>
To: <statalist@hsphsun2.harvard.edu>
Sent: Friday, June 23, 2006 4:15 PM
Subject: st: multiple imputation and life tables

Hi,

I'm new at multiple imputation (using the "ice" command), and I would
appreciate any help you can offer.

I've made 5 imputed datasets for an event-history analysis I am doing.  I
want to use the ltable command to get hazard rates and survival rates, but
ltable is not supported by micombine.  Thus, my output has the 1075*5 =
5375 cases instead of the 1075 cases in the original dataset.

The rates would still be accurate (correct me if I'm wrong), but the
"beginning total", "deaths", and "lost" numbers are not.  Does anyone have
suggestions on how to obtain the actual numbers for these three columns of
data?

Thank you,
Margaret
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