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Re: st: RE: RE: RE: Imputing values for categorical data

From   Richard Goldstein <[email protected]>
To   [email protected]
Subject   Re: st: RE: RE: RE: Imputing values for categorical data
Date   Fri, 16 Apr 2004 10:08:14 -0400

There is a problem with the indicator variable method for missing values. See Jones, MP, (1996) "Indicator and Stratification Methods for Missing Explanatory variables in Multiple Linear Regression," JASA, 91: 222-230.

Rich Goldstein

Dupont, William wrote:


What you propose sounds reasonable to me. However, I recently submitted
a paper that did this and was trashed by a referee, in part because of
how I was handling missing values. Using an indicator variable for
missing values has the advantage as it gives you some idea as to whether
you are, in fact, dealing with nonignorable missing data. However, this
approach does not appear to be in fashion at this time.

My own approach to data analysis is to attempt to use methods that
1. I think are reasonable,
2. are widely accepted within the biostatistical community, and
3. avoid ignoring or attacking sacred cows that are dear to likely

My own sense is that, at least in medical statistics, multiple
imputation is becoming a very popular way of dealing with missing data.
I also feel it is a sensible approach, particularly if it is only used
for confounding variables or if your study design gives you reason to
believe that the missing data is missing at random.

I would be interested to know what other Statalisters think about using
indicator variables to model missing values.


-----Original Message-----
From: bill magee [mailto:[email protected]] Sent: Friday, April 16, 2004 5:11 AM
To: Dupont, William
Subject: RE: RE: RE: Imputing values for categorical data

Hi Bill --
Rather than imputing a missing categorical control or confounding variable, such as gender, wouldn't it usually be better to just include a category for missing (e.g. a dummy for female, a dummy for missing, with male as the excluded contrast group)?

bill magee

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