STATISTICAL MODELS FOR NESTED SERVICES UTILIZATION DATA

正如第94 -060号计划公告（关于方法、测量和统计分析以及心理健康研究的研究）所指出的，“心理健康研究的进展高度依赖于研究人员可用的数据分析策略的质量。考虑到这一点，我们为期三年的项目“嵌套服务利用数据的统计模型”扩展了随机效应回归模型（RRM），以允许在精神卫生服务研究中收集更一般类型的数据。RRM对于分析来自纵向（观察嵌套在受试者中）或集群（受试者嵌套在集群中）设计的数据特别有用，这两种设计在心理健康服务研究中都很常见。在赠款下，我们开发了RRM的名义结果和计数，并制作了软件和手册（称为MIXNO和MIXPREG），实施这些程序并描述其使用。这项工作建立在这个研究小组过去的工作中，连续的，二分的，有序的结果变量的方法和程序已经开发（程序MIXREG和MIXOR）。因此，方法和软件现在可用于纵向或集群设计的广泛类别的结果。这次竞争性更新的重点是进一步推广RRM，以处理集群和纵向数据。例如，可以在嵌套在集群（级别3，例如，医院、诊所、研究单位）。对于这样的3级数据，我们建议推广目前的RRM统计方法，扩展我们的免费软件程序，增强这些程序的用户界面，并开发配套的引物。第二个更基本的统计研究组成部分，这一建议是开始工作的多元RRM。具体来说，我们提出了一个通用的多变量混合效应回归模型，它结合了集群和/或个人特定的时间趋势的随机效应方差分量结构与多个结果变量（可能同时测量多个域的基础响应过程）之间的关联的因子分析模型。该模型还将允许残差自相关。这一新的统计研究领域将详细探讨连续和二元结局指标的情况。因此，本提案的总体目标是进一步发展和推广RRM，以应对分析各种类型的精神卫生服务研究数据时遇到的许多挑战（即，连续的、有序的、名义的、计数），结构（即，单变量或多变量）和各种设计（即，2-级或三级）。

As noted in Program Announcement #94-060, {Research on Methods, Measurement, and Statistical Analysis and Mental Health Research}, "advances in mental health research are highly dependent on the quality of data analytic strategies available to investigators." With this in mind, our three-year project "Statistical Models for Nested Services Utilization Data" extended random-effects regression models (RRM) to allow for more general types of data collected in mental health services research. RRM are especially useful for analyzing data from designs that are longitudinal (observations nested within subjects) or clustered (subjects nested within clusters), both of which are quite common in mental health services research. Under the grant, we developed RRM for nominal outcomes and counts, and produced software and manuals (called MIXNO and MIXPREG) implementing these procedures and describing their use. This work build upon past work of this research team in which methods and programs for continuous, dichotomous, and ordinal outcome variables had been developed (programs MIXREG and MIXOR). Thus, methods and software are now available for a wide class of outcomes for designs that are either longitudinal or clustered. The focus of this competitive renewal is to further generalize RRM to handle data that are both clustered and longitudinal. For example, repeated observations (level-1) may be observed within subjects (level-2) who are nested within clusters (level-3, e.g., hospital, clinic, research unit). For such 3-level data, we proposed to generalize current statistical methodology of RRM, extent our freeware programs, enhance the user interface of these programs, and develop accompanying Primers. A second and more basic statistical research component of this proposal is to begin work on multivariate RRM. Specifically, we propose a general multivariate mixed-effects regression model that combines a random-effects variance component structure for cluster and/or person-specific time trends with a factor analytic model for association between multiple outcome variables (that might simultaneously measure multiple domains of the underlying response process). The model will also allow residual autocorrelation. This new area of statistical research will be explored in detail for the cases of continuous and binary outcome measures. Thus, the overall goal of this proposal is to further develop and generalize RRM to handle many of the challenges encountered in analyzing mental health services research data of various types (i.e., continuous, ordinal, nominal, counts), structures (i.e., univariate or multivariate) and from a variety of designs (i.e., 2-level or 3-level).