The use of finite mixture models to estimate the distribution of the health utilities index in the presence of a ceiling effect

Finite mixture models are flexible parametric models that allow one to describe complex probability distributions as a mixture of a small number of simple probability distributions. Measures of health status are often used to reflect a person's overall health. Such measures may be subject to a ceiling effect, in that the measure is unable to discern gradations in health status above the ceiling. The purpose of this paper is to illustrate the use of finite mixture models to describe the probability distribution of the Health Utilities Index, under the assumption that the HUI is subject to a ceiling effect. Mixture models with two through six components are fit to the HUI. Bayes factors were used to compare the evidence that the Canadian population of non-institutionalized residents is composed of four distinct subpopulations, and that a mixture of six Normal components is required to describe these four subpopulations.