STATISTICAL MODELS FOR NESTED SERVICES UTILIZATION DATA

简介(摘自申请者摘要)：心理健康研究进展 研究高度依赖于数据分析策略的质量 可供调查人员使用。拟议项目的目的是 推广了一类重要的统计模型，即随机效应 回归模型(RRM)，以更好地适应在 心理健康服务研究。RRM特别适用于分析 来自两种嵌套设计的数据：纵向(嵌套观测 对象内)和/或群集式(对象嵌套在群内)设计， 这两种情况在心理健康服务研究中都很常见。因此， 到目前为止，已经开发了随机效果程序来连续、 二分结果变量和顺序结果变量。然而，重要类型的 服务利用率数据未使用以下工具进行最佳或适当的建模 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、住院治疗、社区 心理健康中心等)最好将其视为名义上的结果 变量，而“服务使用频率”或“住院次数” 都很重要。虽然这些变量通常代表了 服务研究，当前的方法不足以 适当地对这些结果进行建模 纵向或集群式设计。此外，即使在适当的统计数据下 方法和软件可用于分析，研究人员未充分利用 这是由于进行这种分析所涉及的复杂性造成的。 该项目将通过专注于发展来解决这些问题， 可实施性和可访问性。开发阶段将重点放在 推广名义和RRM的现行统计方法 Count-type变量。在实现方面，公共领域计算机 将制作能够对标称和 从纵向或集群产生的计数类型响应数据 数据集。为了使这些程序易于访问，基于Windows的用户友好 将实现接口，并将开发附带的入门读物 对于每个程序。因此，该提案的目标是开发和提供 RRM数据分析策略的进一步推广 在分析精神健康服务研究时遇到的挑战 来自纵向和集群设计的数据。

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.