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Distribution-free Inference of Zero-inated Binomial Data for Longitudinal Studies.

ABSTRACT: Count reponses with structural zeros are very common in medical and psychosocial research, especially in alcohol and HIV research, and the zero-inflated poisson (ZIP) and zero-inflated negative binomial (ZINB) models are widely used for modeling such outcomes. However, as alcohol drinking outcomes such as days of drinkings are counts within a given period, their distributions are bounded above by an upper limit (total days in the period) and thus inherently follow a binomial or zero-inflated binomial (ZIB) distribution, rather than a Poisson or zero-inflated Poisson (ZIP) distribution, in the presence of structural zeros. In this paper, we develop a new semiparametric approach for modeling zero-inflated binomial (ZIB)-like count responses for cross-sectional as well as longitudinal data. We illustrate this approach with both simulated and real study data.

SUBMITTER: He H

PROVIDER: S-EPMC4589204 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Distribution-free Inference of Zero-inated Binomial Data for Longitudinal Studies.

He H H Wang W J WJ Hu J J Gallop R R Crits-Christoph P P Xia Y L YL

Journal of applied statistics 20150318 10

Count reponses with structural zeros are very common in medical and psychosocial research, especially in alcohol and HIV research, and the zero-inflated poisson (ZIP) and zero-inflated negative binomial (ZINB) models are widely used for modeling such outcomes. However, as alcohol drinking outcomes such as days of drinkings are counts within a given period, their distributions are bounded above by an upper limit (total days in the period) and thus inherently follow a binomial or zero-inflated bin ...[more]

PMID: 26435563

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