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Activity Number: 155 - Recent Developments in Statistical Methods for Data with Informative Cluster Size
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
Sponsor: ENAR
Abstract #313711
Title: Marginal Analysis of Multiple Outcomes with Informative Cluster Size
Author(s): Aya Mitani* and Elizabeth Kaye and Kerrie P Nelson
Companies: Harvard T. H. Chan School of Public Health and Boston University Henry M. Goldman School of Dental Medicine and Boston University
Keywords: Clustered data; Cluster-weighted GEE; Generalized estimating equations; Multivariate outcomes; Quasi-least squares

In surveillance studies of periodontal disease, the relationship between disease and other health and socioeconomic conditions is of key interest. To determine whether or not a patient has periodontal disease, multiple clinical measurements are taken at the tooth-level. Moreover, patients have varying number of teeth, with those that are more prone to the disease having fewer teeth compared to those with good oral health. Such dependence between the outcome of interest and cluster size (number of teeth) is called informative cluster size and results obtained from fitting conventional marginal models can be biased. We propose a novel method to jointly analyze multiple correlated binary outcomes for clustered data with informative cluster size using the class of generalized estimating equations (GEE) with cluster-specific weights. We compare our proposed multivariate outcome cluster-weighted GEE results to those from the convectional GEE using the baseline data from Veterans Affairs Dental Longitudinal Study. In an extensive simulation study, we show that our proposed method yields estimates with minimal relative biases and excellent coverage probabilities.

Authors who are presenting talks have a * after their name.

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