A pairwise likelihood approach to analyzing correlated binary data

The computational advantages of pairwise likelihood relative to competing approaches are discussed, some efficiency calculations are presented and it is argued that when cluster sizes are unequal a weighted couplewise likelihood should be used for the marginal regression parameters, whereas the unweighted pairwiselihood should be use for the association parameters.

Abstract

The method of pairwise likelihood is investigated for analyzing clustered or longitudinal binary data. The pairwise likelihood is a product of bivariate likelihoods for within cluster pairs of observations, and its maximizer is the maximum pairwise likelihood estimator. We discuss the computational advantages of pairwise likelihood relative to competing approaches, present some efficiency calculations and argue that when cluster sizes are unequal a weighted pairwise likelihood should be used for the marginal regression parameters, whereas the unweighted pairwise likelihood should be used for the association parameters.