Summary. We propose a new approach to fitting marginal models to clustered data when cluster size is in- formative. This approach uses a generalized estimating equation (GEE) that is weighted inversely with the cluster size. We show that our approach is asymptotically equivalent to within-cluster resampling (Hoffman, Sen, and Weinberg, 2001, Biometrika73, 13–22), a computationally intensive approach in which replicate data sets containing a randomly selected observation from each cluster are analyzed, and the resulting estima- tes averaged. Using simulated data and an example involving dental health, we show the superior performa- nce of our approach compared to unweighted GEE, the equivalence of our approach with WCR for large sam- ple sizes, and the superior performance of our approach compared with WCR when sample sizes are small.