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From |
"Joseph Coveney" <jcoveney@bigplanet.com> |

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

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

Date |
Sun, 19 Jul 2009 17:41:59 +0900 |

Stas Kolenikov wrote: 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 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<ariel.linden@gmail.com> 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.:-) -------------------------------------------------------------------------------- Ariel, what kinds of working correlation structure are used in the literature on propensity score weighting in longitudinal data with GEE? If it's all PROC GENMOD; . . . REPEATED SUBJECT= . . . / TYPE=IND; SCWGT . . . ;, then one approach that might be worth considering would be something like -glm . . . [iweight= . . . ], cluster( . . . ) . . .-. According to the user's manual for PROC GENMOD, WEIGHT or SCWGT just divides the dispersion parameter by the observations' SCWGT values. I didn't find anything there about what more, if anything, happens when the REPEATED option is also invoked. If the answer is "not much", then can you mimic PROC GENMOD for those models where the dispersion parameter is one (binomial, Poisson, negative binomial) with the average of the weights (or equivalently its inverse), as in -summarize myweights, meanonly-, -xtgee . . ., corr( . . . ) family(binomial) scale(`r(mean)') [robust]-? The analogous operations for Pearson chi-square or other scales would be something like fitting an unweighted model, weight the resulting scale value by the average of the weights, and then use this as the fixed scale value for a subsequent fit. I suspect that this approach wouldn't given anyone a sense of theoretical gratification--it wouldn't do an observation-by-observation weighting of the dispersion parameter that PROC GENMOD apparently does--but it might suffice in practice. Neither of these would address any of the issues that Stas raises. Joseph Coveney * * 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/

**References**:**st: multiple weights per person in GEE?***From:*"Ariel Linden" <ariel.linden@gmail.com>

**Re: st: multiple weights per person in GEE?***From:*Stas Kolenikov <skolenik@gmail.com>

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