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Fitting mixed models to complex survey data is a challenging problem. Mostmethods in the literature, including the most widely used one, require a closerelationship between the model structure and the survey design. In this paperwe present methods for fitting arbitrary mixed models to data from arbitrarysurvey designs. We support this with an implementation that allows formultilevel linear models and multistage designs without any assumptions aboutnesting of model and design, and that also allows for correlation structuressuch as those resulting from genetic relatedness. The estimation and inferenceapproach uses weighted pairwise (composite) likelihood.

Weighted composite likelihood for linear mixed models in complex samples