Journal article
Marginalized zero-altered models for longitudinal count data
Statistics in biosciences, v 8(2), pp 181-203
Oct 2016
PMID: 27867423
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias.
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Details
- Title
- Marginalized zero-altered models for longitudinal count data
- Creators
- Loni Philip Tabb - Department of Epidemiology & Biostatistics, School of Public Health, Drexel University, Philadelphia, PA, USA Tel.: +267-359-6217 lpp22@drexel.eduEric J Tchetgen Tchetgen - Department of Epidemiology, Harvard School of Public Health, Boston, MA, USAGreg A Wellenius - Department of Community Health, Brown University, Boston, MA, USABrent A Coull - Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
- Publication Details
- Statistics in biosciences, v 8(2), pp 181-203
- Publisher
- Springer Nature; United States
- Grant note
- R35 CA197449 / NCI NIH HHS R01 ES012044 / NIEHS NIH HHS P01 CA134294 / NCI NIH HHS T32 ES007142 / NIEHS NIH HHS P30 ES000002 / NIEHS NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative; Epidemiology and Biostatistics
- Web of Science ID
- WOS:000387280600001
- Scopus ID
- 2-s2.0-84944691377
- Other Identifier
- 991014878497004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology