A hierarchical finite mixture model that accommodates zero-inflated counts, non-independence, and heterogeneity

• Published in: Statistics in medicine Vol. 33; no. 13; pp. 2238 - 2250
• Main Authors: Morgan, Charity J., Lenzenweger, Mark F., Rubin, Donald B., Levy, Deborah L.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 15.06.2014; Wiley Subscription Services, Inc
• Subjects: Adult; Correlation analysis; Datasets as Topic - statistics & numerical data; endophenotype; Endophenotypes; Humans; Medical statistics; Mental health care; Middle Aged; mixture modeling; Models, Statistical; Odds Ratio; Poisson Distribution; psychopathology; Schizophrenia; Schizophrenia - genetics; thought disorder; zero-inflated Poisson regression; mixture modeling; endophenotype; zero-inflated Poisson regression; psychopathology; thought disorder
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6091

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. We present here 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. We demonstrate 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. Copyright © 2014 John Wiley & Sons, Ltd.