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Accounting for drop-out using inverse probability censoring weights in longitudinal clustered data with informative cluster size

Periodontal disease is a serious gum infection impacting half of the US adult population that may lead to loss of teeth. Using standard marginal models to study the association between patient-level predictors and tooth-level outcomes can lead to biased estimates because the independence assumption between the outcome (periodontal disease) and cluster size (number of teeth per patient) is violated. Specifically, the baseline number of teeth of a patient is informative. In this setting, a cluster-weighted generalized estimating equations (CWGEE) approach can be used to obtain unbiased marginal inference from data with informative cluster size (ICS). However, in many longitudinal studies of dental health including the Veterans Affairs Dental Longitudinal Study, the rate of tooth-loss or tooth drop-out over time is also informative, creating a missing at random data mechanism. Here, we propose a novel modeling approach that incorporates the technique of inverse probability censoring weights into CWGEE with binary outcomes to account for ICS and informative drop-out over time. In an extensive simulation study, we demonstrate that results obtained from our proposed method yield lower bias and excellent coverage ∗Supported by NIH grant F31DE027589. We thank the editor and reviewers for their insightful suggestions that led to an improved manuscript. We also acknowledge Professors Howard Cabral and Josée Dupuis for their valuable advice, and Professor Raul Garcia who is the Principal Investigator and examiner for the Dental Longitudinal Study. The Dental Longitudinal Study and Normative Aging Study are components of the Massachusetts Veterans Epidemiology Research and Information Center which is supported by the VA Cooperative Studies Program. Views expressed in this paper are those of the authors and do not necessarily represent the views of the US Department of Veterans Affairs. †Supported by NIH grant R03DE021730. ‡Supported by NIH grant R01CA226805.