Analysis of Pregnancy and Other Factors on Detection of Human Papilloma Virus (HPV) Infection Using Weighted Estimating Equations for Follow-Up Data

Generalised 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 coworkers proposed to use 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 re-analyse data on pregnancy and 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 study subjects after delivery (p =
0.027). We conclude that the WEE are useful in analysing follow-up
data with drop-outs.