Linear mixed models for complex survey data: implementing and evaluating pairwise likelihood

Abstract

As complex-survey data becomes more widely used in health and social-science research, there is increasing interest in fitting a wider range of regression models. We describe an implementation of two-level linear mixed models in R using the pairwise composite likelihood approach of Rao and co-workers. We discuss the computational efficiency of pairwise composite likelihood and compare the estimator to the existing stagewise pseudolikelihood estimator in simulations and in data from the PISA educational survey.