Longitudinal data are commonly encountered in biomedical research, including randomized trials and retrospective cohort studies. Subjects are typically followed over a period of time and may be scheduled for follow-up at pre-determined time points. However, subjects may miss their appointments or return at non-specified times, leading to irregularity in the visit process. Inverse-intensity weighted generalized estimating equations (IIW-GEEs) have been developed as one method to account for this irregularity, whereby estimates from a visit intensity model are used as weights in a GEE model with an independent correlation structure. We have shown that currently available methods can be biased for situations in which the health outcome of interest may influence a subject’s dropout from the study. We have extended the IIW-GEE framework to adjust for informative censoring and have demonstrated via simulation studies that this bias can be significantly reduced.