This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 111
Type: Topic Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #307766
Title: Handling Missingness and Informative Cluster Sizes When Modeling Prevalence and Force of Infection
Author(s): Marc Aerts*+ and Christel Faes and Niel Hens and Geert Molenberghs
Companies: Hasselt University and Hasselt University and Hasselt University and I-BioStat
Address: Agoralaan, Diepenbeek, B3590, Belgium
Keywords: Clustering ; Force of Infection ; Missing Data ; Weighted Generalized Estimating Equations ; Informative Cluster Size ; Within-Cluster Resampling

Missing data and informative cluster sizes are frequently occurring complexities in the analysis of survey data, next to the hierarchical structure and the clustering. Here we focus on marginal analyses. Over the last decade, several methods have been developed to cope with these complexities: WGEE (weighted generalized estimating equations, Zhao and Lipsitz, 1992), WCRGEE (within-cluster resampling GEE, Hoffman et al. 2001) , CWGEE (cluster weighted GEE, Williamson et al. 2003), and modifications thereof (Chiang and Lee, 2008). These will be discussed and applied to seroprevalence data of the bovine herpesvirus-1 in Belgian cattle (Faes et al. 2006, Hens et al. 2007). Recently Molenberghs et al. (2009) formulated a pseudo-likelihood (PL) framework for incomplete data. The PL analogues of the different GEE approaches will be discussed and illustrated.

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