title: Marginal modeling of multilevel binary data with time-varying covariates.
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.
authors.affiliation: Center for Health Studies, Group Health Cooperative, USA.
authors.forename: Diana L
authors.initials: DL
authors.lastname: Miglioretti
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Heagerty
mesh_terms: D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008327:Mammography; D008875:Middle Aged; D012044:Regression Analysis; D012680:Sensitivity and Specificity