Hi, Everyone.
I'd like to comment on the inclusion of X1Y and X2Y interactions in the
imputation
model. For now, I'll assume that such interaction effects will not be tested
in any
substantive model, and so the efficiency of such tests within the multiple
imputation
framework are not a concern. Given that, one might include X1Y and X2Y in
the
imputation model because those effects are believed to represent mechanisms
of
missingness--and thus help to reduce bias. However, given a strict causal
interpretation of the substantive regression model, can this ever be true?
That is,
if X1, X2, and X1X2 all cause Y, then is it possible for, say, X1Y to inform
missingness on X2? Strictly speaking, no. From a practical standpoint,
however,
Y may be a proxy for some variable that is contemporaneous with X2 and
therefore
inclusion of X1Y in the imputation model may be beneficial.
I trust someone will gently stomp on me if I'm missing an important point
here.
Steve
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Steve Gregorich
University of California, San Francisco
Department of Medicine
3333 California Street, Suite 335, Box 0856
San Francisco, CA 94143-0856
(FedEx and UPS use zip code 94118)
[email protected]
http://mywebpage.netscape.com/segregorich/index.html
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-----Original Message-----
>On Wed, 9 Jun 2004 08:04:40 -0400 (Eastern Daylight Time), Rod Little
><[email protected]> wrote:
>
> > Dear Paul: this is an interesting issue. For the specific case you
> > outline, the X1Y interaction should be included; a simple strategy
>would
> > be to simply stratify on X1 and impute Y and X2 separately in the
>two
> > strata. That strategy only applies in limited situations though. Rod
> >
>
>Rod,
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