STATISTICAL MODELS FOR NESTED SERVICES UTILIZATION DATA

描述（改编自申请人的摘要）：心理健康的进展 研究高度依赖于数据分析策略的质量 提供给调查人员。 拟议项目的目的是 扩展了一类重要的统计模型，即随机效应 回归模型（RRM），以更好地适应收集的数据类型 心理健康服务研究。 RRM对于分析 来自两种类型嵌套设计的数据：纵向（嵌套观测 在受试者内）和/或成簇（受试者嵌套在簇内）设计， 这两种情况在心理健康服务研究中都很常见。 因此 迄今为止，随机效应程序已经被开发用于连续的， 二分和有序结果变量。 然而，重要类型的 服务利用率数据没有使用 这些现有的方法。 例如，结果变量“服务类型 使用”（例如，门诊心理治疗，住院，社区 心理健康中心等）最好被当作一个象征性的结果 变量，而“服务使用频率”或“住院次数” 是计数。 虽然这些变量通常代表主要结果， 服务研究，目前的方法不足以 适当地模拟这些结果时，他们产生的， 纵向或集群设计。 此外，即使适当的统计 方法和软件可用于分析，研究人员未充分利用 这种方法是由于执行这种分析所涉及的复杂性。 本项目将通过注重发展来解决这些问题， 执行和可访问性。 开发阶段将侧重于 将目前的名义和 计数类型变量。 在执行方面，公共领域计算机 将产生能够模拟标称和 计数型响应数据产生于纵向或集群 数据集。 为了使这些程序可以访问，一个用户友好的基于Windows的 将建立一个接口，并编写一份附带的入门书 对于每一个节目。 因此，本提案的目标是开发和提供 RRM数据分析策略的进一步推广， 在分析心理健康服务研究中遇到的挑战 数据来自纵向和聚类设计。

DESCRIPTION (adapted from applicant's abstract): Advances in mental health research are highly dependent on the quality of data analytic strategies available to investigators. The purpose of the proposed project is to extend an important class of statistical models, namely random-effects regression models (RRMs), to better accommodate types of data collected in mental health services research. RRMs are especially useful for analyzing data from two types of nested designs: longitudinal (observations nested within subjects) and/or clustered (subjects nested within clusters) designs, both of which are quite common in mental health services research. Thus far, random-effects procedures have been developed for continuous, dichotomous, and ordinal outcome variables. However, important types of services utilization data are not optimally or appropriately modeled using these existing methods. For example, the outcome variable "type of service use" (e.g., outpatient psychotherapy, inpatient hospitalization, community mental health center, etc.) would best be treated as a nominal outcome variable, while "frequency of service use" or "number of hospitalizations" are counts. While these variables often represent the primary outcomes in services research studies, current methodology is insufficient for appropriately modeling these outcomes when they arise from either a longitudinal or clustered design. Also, even when appropriate statistical methods and software are available for analysis, researchers under-utilize such methodology due to the complexity involved in performing such analyses. This project will address these issues by focusing on development, implementation, and accessibility. The development phase will focus on generalizing current statistical methodology of RRM for nominal and count-type variables. In terms of implementation, public domain computer programs will be produced that are capable of modeling nominal and count-type response data arising from either a longitudinal or clustered dataset. To make these programs accessible, a user-friendly Windows-based interface will be implemented and an accompanying primer will be developed for each program. Thus, the goal of this proposal is to develop and provide further generalization of the RRM data analytic strategy for handling many of the challenges encountered in analyzing mental health services research data from longitudinal and clustered designs.