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Virtual over Zoom
  • November 30, 2021 from 3:00pm to 4:00pm

The Biostatistics Seminar Series presents:

“Analysis of Complex Multilevel Dental Data with Informative Tooth-loss” by Dr. Aya Mitani, University of Toronto

Periodontal disease is a serious gum infection that may lead to loss of teeth. Using standard marginal models to examine 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, the rate of tooth-loss over time is also informative, creating a sequentially missing at random data mechanism. In this talk, I will go over the modeling approach that incorporates the technique of inverse probability censoring weights into CWGEE with binary outcomes to account for ICS and informative tooth-loss over time. I will illustrate our methodology using a complex longitudinal data set from the Veterans Affairs Dental Longitudinal Study.

For Dr. Mitani’s biosketch, please see and