Inverse-intensity weighted generalized estimating equations with irregularly measured longitudinal data and informative dropout

George Stefan; Eleanor Pullenayegum

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arXiv · 2510.19154

Inverse-intensity weighted generalized estimating equations with irregularly measured longitudinal data and informative dropout

George Stefan·Eleanor Pullenayegum

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Abstract

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. 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 show 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 dropout and have demonstrated via simulation studies that this bias can be significantly reduced. We have illustrated this method using the STAR*D clinical trial data, and observed that the disease trajectory was generally overestimated when informative dropout was not accounted for.

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George Stefan, Eleanor Pullenayegum. 2025-10-22. Inverse-intensity weighted generalized estimating equations with irregularly measured longitudinal data and informative dropout. https://arxiv.org/abs/2510.19154

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