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Biostatistics (2004), 5, 3, pp. 381-398
Biostatistics Vol. 5 No. 3 © Oxford University Press 2004; all rights reserved.

Marginal modeling of multilevel binary data with time-varying covariates

Diana L. Miglioretti

Center for Health Studies, Group Health Cooperative, USA

Patrick J. Heagerty

Department of Biostatistics, University of Washington, USA

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: Conditional; Endogeneity; Hierarchical models; Longitudinal data; Marginal; Transition models


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