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Re: st: R: Imputation vs substitution with mean


From   Clyde Schechter <clyde.schechter@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: R: Imputation vs substitution with mean
Date   Sat, 19 Oct 2013 08:55:49 -0700

Carlo Lazzaro has advised James Bernard to use MI rather than
substituting means, and characterized last observation carried forward
(LOCF) as "don't do it," and MI as "the way to go." I think these
recommendations need some qualification.

I agree that MI has better statistical properties than mean
substitution and is preferable in most circumstances.  But there are
exceptions.  There are measurement scales that were developed with
ipsative mean imputation of item non-response as part of their design.
 In the modern research setting, one would probably deal with item
non-response through MI instead, but doing so would, in effect, be
changing the design of the measurement and would lose the claim to
rely on any prior validation studies.

I disagree strongly that LOCF is a "don't do it," and that MI is "the
way to go."  If the goal is to get unbiased parameter estimates, then,
certainly LOCF is off the table.  But MI only achieves this goal when
the data are missing at random.  And, unfortunately, missingness at
random is an assumption that can never be tested in the data.  When
one contemplates the mechanisms leading to missing data, in some
studies, (and, in my experience, this is common) missingness at random
may be breathtakingly incredible.  When confronted with data missing
not at random, it is not obvious what MI accomplishes unless you can
somehow base it on a valid model of the missingness mechanisms.  In
this setting, unbiased parameter estimates may be unobtainable by any
means, and LOCF may be one reasonable part of a sensitivity analysis
that seeks to find believable upper and lower bounds on the parameter
estimates.

Clyde Schechter
Department of Family & Social Medicine
Albert Einstein College of Medicine
Bronx, NY, USA
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