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From |
"Ariel Linden" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: multiple weights per person in GEE? |

Date |
Mon, 20 Jul 2009 13:57:06 -0700 |

Thank you Joseph for your input and thoughts. You will see from those references I provided earlier that the IPTW weights are established for each person for each wave. The general model consists of estimating the propensity score at a given wave (the probability of getting treatment at that wave conditional on past characteristics and even past treatment status). This is repeated at each wave for each person. After a propensity score is estimated for each person/wave, the IPTW is then calculated). So there is a specific weight for each person/wave, and it indeed differs. Robins, Hernan and Brumback (2000)* suggest using the SCWGT option in SAS Proc Genmod together with the "repeated" option and specifying am independence working correlation matrix. In Stata, my work-around so far has been to use GLM with the pweight option in which I refer to the IPTW variable I created (as described in the first paragraph above). There is no need to rescale the weights. I have not considered using survey modeling procedures in Stata to handle these data, and I am not sufficiently familiar with the tools to know if they will handle panel data with varying weights. Again, I don't know how SAS GEE models handle varying weights per person/period, but it is available, and researchers use it. Since I am a Stata die-hard, I would hate to have to use SAS because I can't find a solution in Stata. Ariel * Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550?560. Date: Mon, 20 Jul 2009 09:35:33 +0900 From: "Joseph Coveney" <jcoveney@bigplanet.com> Subject: Re: st: multiple weights per person in GEE? 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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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