Mixture Models for Heterogeneity
in Biomedical Disorders
Donna K. Pauler,
Michael D. Escobar, and
John A. Sweeney
Finite mixture analysis is an important tool in the biomedical sciences
for detecting subgroups within populations.
For clinical syndromes that may be composed of discrete subgroups with
distinct pathophysiologies, this technique can be useful for
addressing important questions about heterogeneity. A potential
problem with the application of finite mixture analyses in practice
is that these models may drastically overestimate the number of
component densities. In such
situations a continuous mixture model provides a viable alternative.
In this paper, we apply a mixture analysis to a specific oculomotor
component of eye-tracking
dysfunction in schizophrenia. In the context of this example,
we illustrate some difficulties inherent in the applied use of mixture
analyses and some techniques to overcome these difficulties. We
highlight the importance of type of heterogeneity present in the data
and the role of continuous and finite mixture densities in modeling
each type of heterogeneity.
Keywords: Finite mixture models, negative binomial models,
parametric bootstrap, EM-algorithm, schizophrenia.