He Hua, Tang Wan, Wang Wenjuan, Crits-Christoph Paul
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA ; Veterans Integrated Service Network, Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA ; Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA.
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
Shanghai Arch Psychiatry. 2014 Aug;26(4):236-42. doi: 10.3969/j.issn.1002-0829.2014.04.008.
In psychosocial and behavioral studies count outcomes recording the frequencies of the occurrence of some health or behavior outcomes (such as the number of unprotected sexual behaviors during a period of time) often contain a preponderance of zeroes because of the presence of 'structural zeroes' that occur when some subjects are not at risk for the behavior of interest. Unlike random zeroes (responses that can be greater than zero, but are zero due to sampling variability), structural zeroes are usually very different, both statistically and clinically. False interpretations of results and study findings may result if differences in the two types of zeroes are ignored. However, in practice, the status of the structural zeroes is often not observed and this latent nature complicates the data analysis. In this article, we focus on one model, the zero-inflated Poisson (ZIP) regression model that is commonly used to address zero-inflated data. We first give a brief overview of the issues of structural zeroes and the ZIP model. We then given an illustration of ZIP with data from a study on HIV-risk sexual behaviors among adolescent girls. Sample codes in SAS and Stata are also included to help perform and explain ZIP analyses.
在社会心理和行为学研究中，计数结果记录某些健康或行为结果的发生频率（例如一段时间内无保护性行为的次数），由于存在“结构零值”，往往包含大量零值。当一些受试者没有发生所关注行为的风险时，就会出现结构零值。与随机零值（响应值可以大于零，但由于抽样变异性而为零）不同，结构零值在统计学和临床上通常有很大差异。如果忽略这两种零值的差异，可能会导致对结果和研究发现的错误解读。然而，在实际中，结构零值的情况往往未被观察到，这种潜在性质使数据分析变得复杂。在本文中，我们聚焦于一种模型，即零膨胀泊松（ZIP）回归模型，它常用于处理零膨胀数据。我们首先简要概述结构零值问题和ZIP模型。然后用一项关于少女艾滋风险性行为研究的数据对ZIP模型进行说明。还包括SAS和Stata中的示例代码，以帮助进行和解释ZIP分析。
