Journal ArticleDOI
Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros.
Wondwosen Kassahun,Thomas Neyens,Geert Molenberghs,Geert Molenberghs,Christel Faes,Geert Verbeke,Geert Verbeke +6 more
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Analysis of two datasets showed that accounting for the correlation, overdispersion, and excess zeros simultaneously resulted in a better fit to the data and, more importantly, that omission of any of them leads to incorrect marginal inference and erroneous conclusions about covariate effects.Abstract:
Count data are collected repeatedly over time in many applications, such as biology, epidemiology, and public health. Such data are often characterized by the following three features. First, correlation due to the repeated measures is usually accounted for using subject-specific random effects, which are assumed to be normally distributed. Second, the sample variance may exceed the mean, and hence, the theoretical mean-variance relationship is violated, leading to overdispersion. This is usually allowed for based on a hierarchical approach, combining a Poisson model with gamma distributed random effects. Third, an excess of zeros beyond what standard count distributions can predict is often handled by either the hurdle or the zero-inflated model. A zero-inflated model assumes two processes as sources of zeros and combines a count distribution with a discrete point mass as a mixture, while the hurdle model separately handles zero observations and positive counts, where then a truncated-at-zero count distribution is used for the non-zero state. In practice, however, all these three features can appear simultaneously. Hence, a modeling framework that incorporates all three is necessary, and this presents challenges for the data analysis. Such models, when conditionally specified, will naturally have a subject-specific interpretation. However, adopting their purposefully modified marginalized versions leads to a direct marginal or population-averaged interpretation for parameter estimates of covariate effects, which is the primary interest in many applications. In this paper, we present a marginalized hurdle model and a marginalized zero-inflated model for correlated and overdispersed count data with excess zero observations and then illustrate these further with two case studies. The first dataset focuses on the Anopheles mosquito density around a hydroelectric dam, while adolescents' involvement in work, to earn money and support their families or themselves, is studied in the second example. Sub-models, which result from omitting zero-inflation and/or overdispersion features, are also considered for comparison's purpose. Analysis of the two datasets showed that accounting for the correlation, overdispersion, and excess zeros simultaneously resulted in a better fit to the data and, more importantly, that omission of any of them leads to incorrect marginal inference and erroneous conclusions about covariate effects.read more
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Features of the bronchial bacterial microbiome associated with atopy, asthma, and responsiveness to inhaled corticosteroid treatment
Juliana Durack,Susan V. Lynch,Snehal Nariya,Nirav R. Bhakta,Avraham Beigelman,Mario Castro,Anne Marie Dyer,Elliot Israel,Monica Kraft,Richard J. Martin,David T. Mauger,Sharon R. Rosenberg,Tonya Sharp-King,Steven R. White,Prescott G. Woodruff,Pedro C. Avila,Loren C. Denlinger,Fernando Holguin,Stephen C. Lazarus,Njira L Lugogo,Wendy C. Moore,Stephen P. Peters,Loretta G. Que,Lewis J. Smith,Christine A. Sorkness,Michael E. Wechsler,Sally E. Wenzel,Homer A. Boushey,Yvonne J. Huang +28 more
TL;DR: Even in subjects with mild steroid‐naive asthma, differences in the bronchial microbiome are associated with immunologic and clinical features of the disease, suggesting possible microbiome targets for future approaches to asthma treatment or prevention.
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Marijuana use, craving, and academic motivation and performance among college students: An in-the-moment study.
TL;DR: Using EMA, marijuana craving and use were significantly related, providing further evidence that heavy marijuana use is negatively associated with academic outcomes.
Journal ArticleDOI
Randomized Trial of Motivational Interviewing to Prevent Early Childhood Caries in Public Housing
Michelle M. Henshaw,Belinda Borrelli,Steven E. Gregorich,Brenda Heaton,Erin M. Tooley,William Santo,Nancy F. Cheng,Margaret Rasmussen,S. Helman,S. Shain,Raul I. Garcia +10 more
TL;DR: The results of this study suggest that when the complex disease of early childhood caries is addressed in high-risk populations, MI is not effective, and alternative approaches are warranted.
Journal ArticleDOI
Randomized Trial of Motivational Interviewing to Prevent Early Childhood Caries in American Indian Children.
Terrence S. Batliner,Tamanna Tiwari,William G. Henderson,Anne Wilson,Steven E. Gregorich,Karen Fehringer,Angela G. Brega,E Swyers,T Zacher,Matthew M. Harper,K. Plunkett,William Santo,Nancy F. Cheng,S. Shain,Margaret Rasmussen,Spero M. Manson,Judith Albino +16 more
TL;DR: The findings of this study suggest that motivational interviewing focusing on parental behaviors may not be as effective as previously hoped for slowing the development of childhood caries in some high-risk groups.
Journal ArticleDOI
A marginalized random effects hurdle negative binomial model for analyzing refined-scale crash frequency data
TL;DR: A marginalized random effects hurdle negative binomial (MREHNB) model was developed in which the hurdle modelling structure handles the excessive zeros issue and site-specific random effect terms capture the factors associated with unobserved heterogeneity to obtain the marginal mean inference for the overall population.
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