Good catch Steve! Thank you and Joseph, I think the descrepancy between
Stata's GLM and SAS GEE using weights has been solved.
The results of the SAS GEE seem to match that of Stata's GLM, when you look
at the empirical SE's. Here are the output tables for Stata GLM and SAS
GENMOD (both using the IPTW weights and independence correlation matrix and
cluster(ID):
*** Stata Output***
. glm rx sccenrolled [pweight = iptw], family(gaussian) link(identity)
vce(cluster personnumber)
(Std. Err. adjusted for 7868 clusters in
personnumber)
-------------------------------------------------------------
Robust
rx | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
TX | 2.476136 .4841721 5.11 0.000 1.527176 3.425096
_cons | 1.145776 .0553768 20.69 0.000 1.03724
1.254313
---------------------------------------------------------------
****SAS Output****
GEE Model Information
Correlation Structure Independent
Subject Effect PERSONNU (7868 levels)
Number of Clusters 7868
Clusters With Missing Values 1
Correlation Matrix Dimension 24
Maximum Cluster Size 24
Minimum Cluster Size 23
Analysis Of GEE Parameter Estimates
Empirical Standard Error Estimates
Parameter Estimate Standard Error 95% Confidence Limits Z
Pr > |Z|
Intercept 1.1458 0.0554 1.0372 1.2543 20.69 <.0001
TX 2.4761 0.4841 1.5272 3.4250 5.11 <.0001
Date: Thu, 23 Jul 2009 11:55:54 -0400
From: [email protected]
Subject: Re: st: multiple weights per person in GEE?
Because the SAS output shows the SCALE parameter, I think that Ariel
reported the model-based, not the empirical, standard errors. If I
recall, model-based standard errors ignore the clustering (REPEATED
statement) and so, are just OLS. The ratio of the total sample size
to the number of clusters is about 25:1, with a square root of 5:1.
This would account for some, but not all, of the standard-error
discrepancies. Ariel, please show the second part of the GENMOD
results.
- -Steve
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