Analysis of Multiple Informant Data in Psychiatry

描述（由申请人提供）：精神病理和服务使用的评估通常由社区或基于服务的心理健康研究中的多个线人收集。例如，在评估儿童的心理健康状况时，父母、教师、儿童和其他人都是儿童潜在心理病理的举报人。心理健康治疗的研究和护理质量的评估往往需要多种来源的报告，包括自我报告和查询行政数据库或医疗图表。许多关于精神病学多信息者数据分析的方法学挑战仍未解决。从1995年发表的工作开始，我们开发了一种分析多源数据的一般方法。虽然这些方法已被证明在各种情况下都是有用的，但还需要沿着几个方向进行额外的开发。该应用程序将为分析在社区和基于服务的样本中为测量心理健康结果或风险因素而设计的研究中收集的多个举报人和/或多个评估数据制定方法。在具体目标1中，我们将开发更有效的方法来分析用作预测因子的多个信息者数据，以及开发方法来合并部分观察到的信息者报告。在具体目标2中，我们考虑了在纵向研究中反复测量的多源结局数据的分析方法。在具体目标3中，我们考虑使用多变量潜在变量模型来分析不相称的多源结果。我们提出的创新方法将为多种信息数据的分析提供一个有吸引力的框架，并允许研究人员系统地研究通常是难以衡量的结构（如精神病理学或服务质量）的不同报告。通过将我们的新方法应用于现有的数据集，这些数据集直接适用于最近的几个NIMH计划，我们将把我们的结果传播给心理健康研究界。除了描述我们新方法的出版物外，我们还将在现有的多信息者网页上提供宏、文档和教程，并在心理健康和服务会议上提供短期课程。

DESCRIPTION (provided by applicant): Assessments of psychopathology and service use are commonly collected using multiple informants in community or service-based mental health research. For example, in assessments of a child's mental health status, parents, teachers, children and others have served as informants on the underlying psychopathology of the child. Studies of mental health treatment and evaluations of quality of care often solicit multiple source reports, including self-report and queries of administrative databases or medical charts. Many methodological challenges regarding the analysis of multiple informant data in psychiatry remain unsolved. Beginning with work published in 1995, we have developed a general approach to the analysis of multiple source data. While these methods have proven useful in a variety of settings, additional development is needed along several lines. This application will develop methodology for the analysis of multiple informants and/or multiple assessment data collected in studies designed to measure mental health outcomes or risk factors in community and service-based samples. In Specific Aim 1, we will develop more efficient methods for the analysis of multiple informant data used as predictors, as well as develop methods to incorporate partially observed informant reports. In Specific Aim 2, we consider methods for the analysis of multiple source outcome data measured repeatedly in longitudinal studies. In Specific Aim 3, we consider the use of multivariate latent variable models for the analysis of multiple source outcomes that are non-commensurate. The innovative methodology that we propose will provide an attractive framework for the analysis of multiple informant data, and allow researchers to systematically study often disparate reports on a construct (such as psychopathology or quality of services) that is inherently difficult to measure. By applying our new methodology to existing datasets that are directly applicable to several recent NIMH initiatives, we will disseminate our results to the mental health research community. In addition to publications that describe our new methodology, we will provide macros, documentation and tutorials on our existing multiple informant web page, and present short courses at mental health and services conferences.