Marginalized Zero-Altered Models for Longitudinal Count Data
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- 作者：Loni Philip Tabb ; Eric J. Tchetgen Tchetgen ; Greg A. Wellenius…
- 关键词：Longitudinal data ; Marginal regression ; Negative binomial ; Poisson ; Zero inflation
- 刊名：Statistics in Biosciences
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：8
- 期：2
- 页码：181-203
- 全文大小：522 KB
- 刊物类别：Mathematics and Statistics
- 刊物主题：Statistics
Statistics for Life Sciences, Medicine and Health Sciences
Biostatistics
Theoretical Ecology - 出版者：Springer New York
- ISSN：1867-1772
- 卷排序：8
文摘
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.