Generalized estimating equations have been well established to draw inference for the marginal mean from follow-up data. Many studies suffer from missing data that may result in biased parameter estimates if the data are not missing completely at random. Robins and co-workers proposed using weighted estimating equations (WEE) in estimating the mean structure if drop-out occurs missing at random. We illustrate the differences between the WEE and the commonly applied available case analysis in a simulation study. We apply the WEE and reanalyse data of a longitudinal study of pregnancy and human papilloma virus (HPV) infection. We estimate the response probabilities and demonstrate that the data are not missing completely at random. Upon use of the WEE, we are able to show that pregnant women have an increased odds for an HPV infection compared with non-pregnant women after delivery (p=0.027). We conclude that the WEE are useful for dealing with monotone missing data due to drop-outs in follow-up data.
Copyright 2003 John Wiley & Sons, Ltd.