A number of mixture modeling approaches assume both normality and independent observations. However, these two assumptions are at odds with the reality of many data sets, which are often characterized by an abundance of zero-valued or highly skewed observations as well as observations from biologically related (i.e., non-independent) subjects. Dr. Charity J. Morgan, assistant professor in the department of biostatistics at the University of Alabama at Birmingham, presented a finite mixture model with a zero-inflated Poisson regression component that may be applied to both types of data.
This flexible approach allows the use of covariates to model both the Poisson mean and rate of zero inflation and can incorporate random effects to accommodate non-independent observations. Dr. Morgan and her team demonstrated the utility of this approach by applying these models to a candidate endophenotype for schizophrenia, but the same methods are applicable to other types of data characterized by zero inflation and non-independence. “A Hierarchical Finite Mixture Model that Accommodates Zero-Inflated Counts, Non-independence, and Heterogeneity” was published in June in Statistics in Medicine.