Online Program

Too many zeros, too long a tail: Mixed-effects, mixed-distribution modeling as a powerful approach to analyze public health data

Tuesday, November 5, 2013 : 3:00 p.m. - 3:15 p.m.

Blake Barrett, MSPH, Department of Community and Family Health, College of Public Health, University of South Florida, Tampa, FL
John Ferron, PhD, Department of Educational Measurement and Research, College of Education, University of South Florida, Tampa, FL
M. Scott Young, PhD, Department of Mental Health Law & Policy, Florida Mental Health Institute, University of South Florida, Tampa, FL
Count data are common in public health research and statistical modeling. Poisson-based regressions offer a powerful method to analyze count data that can be incorporated in multilevel or hierarchical analyses to help address violations of assumptions for Ordinary Least Squares regression models. The assumption of equidispersion in Poisson models is often not met in real data, typically due the presence of overdispersion. Negative binomial models may be used to more appropriately examine overdispersed data, yet researchers may still encounter difficulties in study modeling due to the presence of ‘excessive' zeros. While a variety of zero-inflated Poisson (ZIP) as well as zero-inflated negative binomial (ZINB) models exist to address the presence of excessive zeros and overdispersion, these models may not be the most appropriate for all public health data. Specifically, outcome data that are semi-continuous in nature – with large number of zeros and high degree of skew among non-zero values – may be most appropriately modeled via mixed-effects, mixed-distribution (MEMD) modeling. MEMD accounts for violations of distributional assumptions in Poisson-based regression models. Similar to ZIP and ZINB, MEMD simultaneously estimates models for both any occurrence of the outcome (logistic regression) as well as intensity among those with any occurrence (Poisson regression), along with their covariance. This study: 1) presents an overview of different modeling approaches for analyzing public health count data, 2) discusses distributional assumptions and their implications for choosing the most appropriate model, and 3) provides a practical example of MEMD modeling using real public health data.

Learning Areas:

Biostatistics, economics
Conduct evaluation related to programs, research, and other areas of practice
Program planning
Public health or related research
Social and behavioral sciences

Learning Objectives:
List distributional assumptions of Poisson-based models for count data. Identify different types of semi-continuous public health count data. Discuss the use of mixed-effects, mixed-distribution modeling for analyzing semi-continuous public health count data.

Keyword(s): Statistics, Substance Abuse Treatment

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have been involved as a drug court and public health researcher and evaluator for the past six years. I am currently a doctoral student at the College of Public Health at the University of South Florida and have received extensive statistical and methodological training. This study presents one innovative statistical approach to powerfully analyze drug court and other types of public health interventions where the dependent variable is count data.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.