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Re: st: multiple weights per person in GEE?


From   sjsamuels@gmail.com
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: multiple weights per person in GEE?
Date   Sun, 19 Jul 2009 22:31:27 -0400

--

The WEIGHT used by SAS GENMOD is a dispersion parameter weight, which
should be equivalent to Stata's -aweight-.   Weights for IPTW should
be proportional to probability weights, as in the reference Joseh
quotes. Ariel did not show us her Stata commands and output, as the
Statalist FAQ specify, but it's quite likely that she is comparing
different  things.

-Steve

On Sun, Jul 19, 2009 at 8:35 PM, Joseph Coveney<jcoveney@bigplanet.com> wrote:
> Ariel Linden wrote (excerpted):
>
> I have been using GLM with vce(cluster) with the IPTW weight, but the SE is
> much larger than that produced in SAS using GEE. For example, with a beta
> coeficient for a treatment variable of 2.47, GLM in stata gives me a SE of
> 0.484 (CI = 1.53, 3.43) while GEE in SAS gives me SE of 0.013 (CI = 2.45,
> 2.50).
>
> This is a pretty meaningful difference, and in several models this can
> change the treatment effect from being positive to one of non significance.
>
> --------------------------------------------------------------------------------
>
> I take it that the difference you're seeing in SEs with identical point
> estimates is between
>
> PROC GENMOD;
> . . .
> REPEATED SUBJECT = . . . / TYPE = IND;
> SCWGT . . .;
>
> and
>
> glm . . . [<weight>= . . .], cluster(. . .) . . .
>
> If so, then these are indeed larger differences than would be expected if the
> two packages mean the same thing by "weight" in this context.  You've probably
> already considered the following and more, but just in case:
>
> 1. What kind of weights are you declaring the IPTW to be in Stata?  Fewell et
> al. (2004) used Stata's -pweight-.
>
> 2. Related to that, does PROC GENMOD need scaling of the weights so that they
> sum to the number of observations?
>
> 3. Is it possible to cajole Stata into allowing the time-varying weights that
> you want by viewing the observation time points in the same manner as waves of a
> survey and setting the model up as a survey data analysis task?
>
> Joseph Coveney
>
> Z. Fewell, M. A. Hernán, F. Wolfe, K. Tilling, H. Choi, J. A. C. Sterne.  2004.
> Controlling for time-dependent confounding using marginal structural models.
> _The Stata Journal_ 4(4):402–420.
>
>
>
>
> *
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>



-- 
Steven Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
845-246-0774

*
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