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.
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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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