Mixed-Effects ZIP Models--Mental Health Service Research

描述(申请人提供)：精神卫生服务研究 由于缺乏适当的统计方法，利用率受到影响。使用 混合效应回归模型的出现(Laird和Ware，1982)， 这些数据的复杂多水平抽样性质(即纵向和/或 整群抽样设计)可以在统计分析中使用。 这代表着这一领域的重大进步(见Gibbons等人)。1993年，适用于 在心理健康研究背景下的概述)。不过，现在 传统的混合效应回归模型不能适应 将服务利用率视为主要结果衡量标准的复杂性 利息。在大多数情况下，服务访问被列举并作为连续使用 和假设的正态分布响应度量 适当的固定效应或混合效应回归模型。当然，一个 通常，大部分受试者从未使用过服务，而少数受试者 主体是服务的大众消费者。由此得到的分布是， 任何不正常和数据转换都不能有效地实现 概率峰值为零的非负分布的正态分布。 替代方案包括：(1)忽略数据的数量性质，并 将服务使用作为一个二元结果进行分析；(2)创建有序响应 变量具有类别，例如，零访问、1访问、2访问、3 访问次数，4次或4次以上；(3)将计数建模为泊松分布 泊松固定效应或混合效应回归模型。这些选项，尽管 他们的统计复杂性，都是对现实的有限看法 服务利用率数据。二元方法简单地丢弃了量化的 调查员不厌其烦地收集的信息。序数 方法依赖于通常不切实际或任意的切入点，通常 假设协变量对类别有成比例的影响。这个 泊松分布往往不能很好地适应心理健康服务 利用率数据，因为它低估了没有 使用服务。这些启发式方法的一个有用的替代方法是建模 数据为零膨胀的泊松分布(Lambert，1992)。在 回归模型的背景，零膨胀泊松或“ZIP”模型 允许估计使用或不使用的一组回归系数 服务和一组单独的回归系数 使用的服务，视其使用情况而定。最终结果是直觉上的 一种吸引人的模式，允许心理健康服务研究人员 同时以二进制形式调查服务利用率的决定因素 变量以及这些相同或不同的解释变量的程度 预测那些使用服务的个人的使用量。 本研究的主要目标是将ZIP模型完全扩展到 混合影响情况，以便分析纵向和/或集群式服务 利用率数据是可能的。除了统计的发展之外 理论和评估程序，我们建议开发基于Windows的免费软件 从我们的网站分发，并将该方法应用于 三个大型精神卫生服务研究数据库的分析。

DESCRIPTION (provided by applicant): The study of mental health service utilization is compromised by the lack of adequate statistical methods. With the advent of mixed- effects regression models (Laird and Ware, 1982), the complex multi-level sampling nature of these data (i.e. longitudinal and/or clustered sampling designs) can be accommodated in the statistical analysis. This represents a major advance in this field (see Gibbons et.al. 1993 for an overview in the context of mental health research). Nevertheless, now traditional mixed-effects regression models fail to accommodate the complexities of viewing service utilization as a primary outcome measure of interest. In most cases, service visits are enumerated and used as continuous and putatively normally distributed response measure in an otherwise appropriate fixed-effects or mixed-effects regression model. Of course, an often large proportion of the subjects never utilize services, whereas a few subjects are mass consumers of services. The resulting distribution is, anything but normal and data transformations are ineffective at bringing about normality for nonnegative distributions with a probability spike at zero. Alternatives include, (1) ignore the quantitative nature of the data and analyze service use as a binary outcome; (2) Create an ordinal response variable with categories of, for example, zero visits, 1 visit, 2 visits, 3 visits, 4 or more visits; (3) model the counts as a Poisson distribution in a Poisson fixed-effects or mixed-effects regression model. These options, despite their statistical sophistication, are all limited views of the reality of service utilization data. The binary approach simply discards the quantitative information that the investigator went to the trouble to collect. The ordinal approach relies on often unrealistic or arbitrary cut-points and typically assumes that the covariates have a proportional effect over the categories. The Poisson distribution often fails to adequately fit mental health service utilization data in that it underestimates the number of subjects who do not use services. A useful alternative to these heuristic approaches is to model the data as a zero-inflated Poisson distribution (Lambert, 1992). In the context of a regression model, the zero- inflated Poisson or "ZIP" model allows one to estimate one set of regression coefficients for use or non-use of services and a separate set of regression coefficients for the amount of services used, conditional on their use. The net result is an intuitively appealing model which allows mental health services researchers to simultaneously investigate the determinants of service utilization as a binary variable and the degree to which those same or different explanatory variables predict the amount of utilization for those individuals who utilize services. The primary objective of this research is to fully extend the ZIP model to the mixed-effects case, so that analysis of longitudinal and/or clustered service utilization data is possible. In addition to development of the statistical theory and estimation procedure, we propose to develop WINDOWS based freeware to be distributed from our web site and to apply the methodology in the analysis of three large mental health services research databases.