Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwoord Ave, Rochester, NY 14642, USA.
Stat Med. 2013 Jun 30;32(14):2390-405. doi: 10.1002/sim.5691. Epub 2012 Dec 12.
Overdispersion and structural zeros are two major manifestations of departure from the Poisson assumption when modeling count responses using Poisson log-linear regression. As noted in a large body of literature, ignoring such departures could yield bias and lead to wrong conclusions. Different approaches have been developed to tackle these two major problems. In this paper, we review available methods for dealing with overdispersion and structural zeros within a longitudinal data setting and propose a distribution-free modeling approach to address the limitations of these methods by utilizing a new class of functional response models. We illustrate our approach with both simulated and real study data.
过度离散和结构零是在使用泊松对数线性回归对计数响应进行建模时偏离泊松假设的两种主要表现形式。大量文献指出，忽略这些偏差可能会导致偏差，并得出错误的结论。已经开发了不同的方法来解决这两个主要问题。在本文中，我们回顾了在纵向数据设置中处理过度离散和结构零的现有方法，并提出了一种无分布建模方法，通过利用一类新的功能响应模型来解决这些方法的局限性。我们使用模拟和真实研究数据来说明我们的方法。
