Marginal Modeling of Multilevel Binary Data with Time-Varying Covariates

Diana Miglioretti and Patrick Heagerty
Additional contact information
Diana Miglioretti: Group Health Cooperative
Patrick Heagerty: University of Washington

No 1050, UW Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three-step method for obtaining empirical standard errors; and a likelihood-based method implemented using Bayesian computational techniques. Implications of time-varying endogenous covariates are addressed. The methods are illustrated using data from the Breast Cancer Surveillance Consortium to estimate mammography accuracy from a repeatedly screened population.

Keywords: longitudinal data; endogeneity; conditional; marginal; transition; models; hierarchical models (search for similar items in EconPapers)
Date: 2004-07-11
Note: oai:bepress.com:uwbiostat-1050
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Citations: View citations in EconPapers (5)

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