Hi Stan,
Thank you for your response and the link. Below, I provide you with a couple
of benchmark studies that discuss the use of inverse probability of
treatment weights (IPTW) for longitudinal data models using the GEE
(refering to these models as marginal structural models). As you will see,
it is important that person/period weights be allowed to vary. In
point-estimate studies, GLM models are typically used for estimation with
the IPTW, and of course, there is only one weight per person. The issue is
how to deal with differing weights for each panel in panel data?
Incidentally, xtreg also does not allow weights to vary by person/periods.
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.
Not that I have voting rights, but I would argue that, given that more
researchers are using IPTW weights in longitudinal models, it is probably
time to allow for weights to vary by person/periods.
Hernan MA, Brumback BA, Robins JM. Estimating the causal effect of
zidovudine on CD4 count with a
marginal structural model for repeated measures. Statistics in Medicine
2002; 21:1689 -1709.
Robins JM. Marginal structural models. In: 1997 Proceedings of the section
on
Bayesian statistical science. Alexandria, VA: American Statistical
Association
1-10, 1998.
Thanks!
Ariel
From: Stas Kolenikov <[email protected]>
Subject: Re: st: multiple weights per person in GEE?
If SAS does it, it does not mean it is such a great idea. And propensity
score matching people rarelly care about any other complications that may be
arising from the complex data structure, in my experience.
First, check out the FAQ:
http://www.stata.com/support/faqs/stat/xtweight.html
<http://www.stata.com/support/faqs/stat/xtweight.html> which talks about
the conceptual foundations for use of weights. Propensity score weights are
neither frequency, variance, or sampling weights; they are more like kernel
weights in non-parametric regression.
At any rate, my understanding of GEE is that a contribution to the objective
function is from the whole panel: you compute the residuals, then, for each
panel, you compute the quadratic form with the residuals using the working
correlation matrix, and then the whole result is multiplied by the weight
and added to the total. How exactly would the different weights go into that
quadratic form? SAS might have found some algorithmic implementation (e.g.,
multiply each residual by the square root of the weight before wrapping the
residuals around the correlation matrix), but I would personally want to see
a Biometrika paper that would justify this before I apply any such method.
On Fri, Jul 17, 2009 at 11:40 AM, Ariel Linden<[email protected]>
wrote:
> This is a question more directed at the Stata folks than to the
> listserve per se.
>
> Is there a reason why xtgee does not allow different
> weights/person/wave? It gives an error message stating "weight must be
constant within personnumber"
>
> While I hate to invoke the phrase, "but SAS does it", I am forced to.
> There is a growing body of literature in which the propensity score
> weighting method is applied to longitudinal data. Thus, by it's very
> nature, weights will differ within individuals over each wave.
>
> I recogize GLLAMM as an option, but it is not very user friendly and
> inordinately slower than other models within this family.
>
> Consider this a plea for improvement.:-)
>
> Thanks
>
> Ariel
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