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.
Categorical Data Analysis | Longitudinal Data Analysis and Time Series | Statistical Models
Miglioretti, Diana and Heagerty, Patrick, "Marginal Modeling of Multilevel Binary Data with Time-Varying Covariates" (December 2003). UW Biostatistics Working Paper Series. Working Paper 218.