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Innovative Statistical Methods for Public Health Data pp 97–115Cite as

On the Effects of Structural Zeros in Regression Models

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Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

Count variables are commonly used in public health research. However, the count variables often do not precisely capture differences among subjects in a study population of interest. For example, drinking outcomes such as the number of days of any alcohol drinking (DAD) over a period of time are often used to assess alcohol use in alcohol studies. A DAD value of 0 for a subject could mean that the subject was continually abstinent from drinking such as lifetime abstainers or that the subject was alcoholic, but happened not to use any alcohol during the period of time considered. In statistical analysis, zeros of the first kind are referred to as structural zeros, to distinguish them from the second type, sampling zeros. As the example indicates, the structural and sampling zeros represent two groups of subjects with quite different psychosocial outcomes. Although many recent studies have begun to explicitly account for the differences between the two types of zeros in modeling drinking variables as responses, none have acknowledged the implications of the different types of zeros when such drinking variables are used as predictors. This chapter is an updated version of He et al. (J Data Sci 12(3), 2014), where we first attempted to tackle the issue and illustrate the importance of disentangling the structural and sampling zeros in alcohol research using simulated as well as real study data.

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Acknowledgements

The study was supported in part by National Institute on Drug Abuse grant R33DA027521, National Institute of General Medical Sciences R01GM108337, Eunice Kennedy Shriver National Institute of Child Health and Human Development grant R01HD075635, UR CTSI 8UL1TR000042-09, and a Faculty Research Support Grant from School of Nursing, University of Rochester.

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Authors and Affiliations

  1. Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 265 Crittenden Blvd., Box 630, Rochester, NY, 14642, USA

    Hua He, Wenjuan Wang & Wan Tang

  2. School of Social Work, University of North Carolina, Chapel Hill, NC, 27599, USA

    Ding-Geng (Din) Chen

Authors
  1. Hua He
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  2. Wenjuan Wang
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  3. Ding-Geng (Din) Chen
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  4. Wan Tang
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Corresponding author

Correspondence to Hua He .

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Editors and Affiliations

  1. Wallace H. Kuralt Distinguished Professor, Director of Statistical Development and Consultation, School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

  2. Arizona State University, Tempe, Arizona, USA

    Jeffrey Wilson

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He, H., Wang, W., Chen, DG., Tang, W. (2015). On the Effects of Structural Zeros in Regression Models. In: Chen, DG., Wilson, J. (eds) Innovative Statistical Methods for Public Health Data. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18536-1_6

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