Marginal modeling of multilevel binary data with time-varying covariates

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.