STATISTICAL MODELS FOR NESTED SERVICES UTILIZATION DATA

正如项目公告#94-060中指出的那样，“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)，以允许在心理健康服务研究中收集更一般类型的数据。RRM在分析纵向(观察嵌套在受试者中)或聚集式(受试者嵌套在簇中)设计的数据时特别有用，这两种设计在心理健康服务研究中都很常见。根据赠款，我们开发了名义结果和计数的RRM，并制作了实施这些程序并描述其用途的软件和手册(称为MIXNO和MIXPREG)。这项工作建立在这个研究团队过去的工作基础上，在这些工作中，已经开发了用于连续、二分和有序结果变量的方法和程序(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。此次竞争性更新的重点是进一步推广RRM，以处理既是集群又是纵向的数据。例如，可以在嵌套在集群(3级，例如医院、诊所、研究单位)内的受试者(2级)内观察到重复观察(1级)。对于这类三级数据，我们建议推广现有的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，并开发配套的入门读物。这项建议的第二个也是更基本的统计研究部分是开始研究多变量RRM。具体地说，我们提出了一个通用的多变量混合效应回归模型，该模型结合了集群和/或个人特定时间趋势的随机效应方差分量结构和用于多个结果变量之间关联的因素分析模型(可能同时测量潜在反应过程的多个领域)。该模型还将允许残差自相关。这一新的统计研究领域将针对连续和二元结果计量的情况进行详细探讨。因此，这项建议的总体目标是进一步发展和推广RRM，以应对在分析各种类型(即，连续、有序、名义、计数)、结构(即，单变量或多变量)和来自各种设计(即，2-水平或3-水平)的精神健康服务研究数据时遇到的许多挑战。

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).