A mixture model with random-effects components for classifying sibling pairs

In healthy aging research, typically multiple health outcomes are measured, representing health status. The aim of this paper was to develop a model-based clustering approach to identify homogeneous sibling pairs according to their health status. Model-based clustering approaches will be considered on the basis of linear mixed effect model for the mixture components. Class memberships of siblings within pairs are allowed to be correlated, and within a class the correlation between siblings is modeled using random sibling pair effects. We propose an expectation-maximization algorithm for maximum likelihood estimation. Model performance is evaluated via simulations in terms of estimating the correct parameters, degree of agreement, and the ability to detect the correct number of clusters. The performance of our model is compared with the performance of standard model-based clustering approaches. The methods are used to classify sibling pairs from the Leiden Longevity Study according to their health status. Our results suggest that homogeneous healthy sibling pairs are associated with a longer life span. Software is available for fitting the new models.