142nd APHA Annual Meeting and Exposition

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304037
Untangle Structural and Random Zeros in Statistical Modelling

142nd APHA Annual Meeting and Exposition (November 15 - November 19, 2014): http://www.apha.org/events-and-meetings/annual
Wednesday, November 19, 2014 : 11:30 AM - 11:50 AM

Hua He , Department of Biostatistics and Computational Biology, University of Rochester, Rochester,, NY
Wan Tang , Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY
Wenjuan Wang
Naiji Lu, PhD , Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY
Din Chen, Professor , School of Nursing, University of Rochester, Rochester, NY
Count responses with structural zeros are common in social behavior studies. There are much research activities focusing on zero-inflated models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) models to model such zero-inflated count responses. However, when such variables are used as covariates or predictors, the differences between structural and random zeros are often ignored. Such an approach may produce biased estimate and hence yield false conclusions. One remedy is to include an indicator for the structural zeros in the model as predictor. However, the structural zeros are not observed in most cases,  and no statistical methods are available to address this issue. This paper is then aimed to fill this methodological gap to develop parametric models to incorporate zero-inflated count models from the covariate and make statistical inference based on the theory of maximum likelihood estimation methods. The response variable can be any type of data including continuous, binary, and count or zero-inflated count responses. Simulation studies are performed to assess the numerical performance of this novel approach when sample size is small and moderate.  
    Key words: generalized linear models; maximum likelihood method; structural zeros; zero-inflated Poisson;

Learning Areas:

Biostatistics, economics
Epidemiology
Public health or related research
Social and behavioral sciences

Learning Objectives:
Define a parametric statistical models for Count independent variable with structural zeros Discuss how the new parametric models incorporate zero-inflated count models from the covariate and make statistical inference based on the theory of maximum likelihood estimation methods. Evaluate the performance of the new approach by simulation studies Analyze the NHANES 2009-2010 data using the new approach

Keyword(s): Behavioral Research, Alcohol Use

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have been co-investigator or biostatistican of multiple federally funded grants focusing on psychosocial behavior such as alcohol, depression and HIV/AIDs research.The issues of structural zeros are very common in these fields. One of my scientific interests has been the development of statistical models for addressing these issues to promote the research in these areas.
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.

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