Pseudo-likelihood methodology for hierarchical count data

Abstract

Generalized Estimating Equations (GEE) are a widespread tool for modeling corelated data, based on properly formulating a marginal regression function, combined with working assumptions about the correlation function. Should interest be placed in addition on the correlation function, then, apart from second-order GEE, pseudo-likelihood (PL) also provides an attractive alternative, especially in its pairwise form, where the covariance between each pair of the response vector is modeled as well. An elegant PL approach is formulated in this paper, based on a flexible bivariate Poisson model. The performance of the PL-method is studied, relative to GEE, using simulations. Data on repeated counts of epileptic seizures in a two-arm clinical trial are analyzed. A macro has been developed by the authors and made available on their web pages.