# GEE with Inverse Probability Weights

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## GEE with Inverse Probability Weights

 Greetings, I have a very, very, simple research question.  I want to predict one dichotomous variable using another dichotomous variable.  Straightforward, right?  The issue is that the dataset has two issues causing some complications for me. 1) The subjects are not independent -- they are sibling pairs.  Every person in the dataset has a sibling in the dataset.  This needs to be treated a nuisance for the purposes of my analysis. 2) The subjects were not sampled randomly.  Some of the subjects had a higher probability of selection, and I want to incorporate inverse-probability weights into my analysis to account for this.  (The inverse-probability weights are already calculated). I know that GEE is an appropriate technique to deal with Issue #1, and I've toyed with the gee pack in R.   R> library("gee") http://cran.r-project.org/web/packages/gee/gee.pdfMy question is -- how can I incorporate the sampling weights into the GEE code?  I don't see a spot for it based on the documentation here, unless I'm missing something obvious.  Or is there another GEE function I can use that would allow me to do this?   Thanks!
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## Re: GEE with Inverse Probability Weights

 On Wed, Jun 13, 2012 at 9:25 AM, RFrank <[hidden email]> wrote: > Greetings, > > I have a very, very, simple research question.  I want to predict one > dichotomous variable using another dichotomous variable.  Straightforward, > right?  The issue is that the dataset has two issues causing some > complications for me. > > 1) The subjects are not independent -- they are sibling pairs.  Every person > in the dataset has a sibling in the dataset.  This needs to be treated a > nuisance for the purposes of my analysis. > 2) The subjects were not sampled randomly.  Some of the subjects had a > higher probability of selection, and I want to incorporate > inverse-probability weights into my analysis to account for this.  (The > inverse-probability weights are already calculated). > > I know that GEE is an appropriate technique to deal with Issue #1, and I've > toyed with the gee pack in R. > R> library("gee") > http://cran.r-project.org/web/packages/gee/gee.pdf> > My question is -- how can I incorporate the sampling weights into the GEE > code?  I don't see a spot for it based on the documentation here, unless I'm > missing something obvious.  Or is there another GEE function I can use that > would allow me to do this? You don't need GEE; you can simply use logistic regression with sampling weights and an appropriate description of the sampling design. eg library(survey) mydesign <- svydesign(id=~sib.pair.id, weights=~sampling.weights, data=mydataset) svyglm( response~predictor, family=quasibinomial(), design=mydesign)    -thomas -- Thomas Lumley Professor of Biostatistics University of Auckland ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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## Re: GEE with Inverse Probability Weights

 Thanks -- extremely helpful.  But what is the mechanism by which this analysis corrects for the fact that my subjects are clustered (twins)?