ANALYSIS OF MULTIPLE INFORMANT DATA IN PSYCHIATRY

这一建议将制定方法，分析旨在衡量社区和服务样本中心理健康结果或风险因素的调查中收集的多个举报人和/或多个评估数据。使用多重举报人（家长、教师、儿童、临床医生、同伴）通常被认为是获得有关儿童心理健康、社会功能和服务使用信息的最佳方法。多重来源数据在其他精神病学研究中也很常见（例如，多重精神病学评估）。在合并来自多个来源的数据时遇到的一些困难包括：来源之间关于某些结果的系统性差异、举报人的一致程度相对较低，以及某些来源的信息缺失。从1995年发表的工作开始，我们开发了一种针对分类结果的多源数据分析的一般方法。具体来说，我们已经开发了将多个源数据合并到逻辑回归框架中的方法，其中结果或预测因子可能来自多个源。这些方法适应了在一个或多个来源中缺失响应的可能性。目前的建议寻求进行这方面的研究。首先，我们将为使用多个来源来确定风险因素的情况开发一个通用回归模型，该模型建立在我们之前在此设置中的工作基础上。这个模型在概念上类似于我们最初的回归模型，由多个来源测量结果。它将允许在简化的模型拟合中使用所有可用的数据，研究不同来源的影响，并包括部分缺失数据的主题。我们将通过展示如何扩展我们的工作以适应其他类型的结果数据（包括连续和反向多源数据）来完成现有的方法。我们将重新讨论我们提出的无反应方法，以说明替代方法如何可能有用。我们将开发新的方法来处理在两阶段设计中获得的数据分析。最后，我们将开发方法的扩展，可用于分析纵向研究中反复获得的多源数据。我们将开发适当的软件，以便这些方法学的发展可以随时用于精神病学研究。我们将设计可以与现有的、广泛可用的包一起使用的宏和过程。我们将把我们的方法应用于五个现有的数据集；四个数据集来自正在进行的研究，这些研究涉及与该调查小组的合作。所提出的方法也将直接适用于NIMH最近的几项倡议。

This proposal will develop methodology for the analysis of multiple informants and/or multiple assessment data collected in surveys designed to measure mental health outcomes or risk factors in community and service-based samples. The use of multiple informants (parents, teachers, children, clinicians, peers) is generally regarded as the best approach to obtain information about children's mental health, social functioning, and service use. Multiple sources data are also common in others of psychiatric research (e.g. multiple psychiatric assessments). Some of the difficulties encountered in combining data from multiple sources include systematic differences between sources with regard to some outcomes, a relatively low level of informant agreement, and missing information for some sources. Beginning with work published in 1995, we have developed a general approach to the analysis of multiple source data focusing on categorical outcomes. Specifically, we have developed methods for incorporating multiple source data into a logistic regression framework where either the outcomes, or the predictors, may arise from multiple sources. These methods accommodate the possibility for missing responses in one or more sources The current proposal seeks to pursue this line of research. First, we will develop a general regression model for the case where multiple sources are used to determine risk factors and which builds on our previous work in this setting. This model will be conceptually similar to our initial regression model with outcomes measured by multiple sources. It will allow the use of all available data in parsimonious model fitting, the study of the effects of different sources, and the inclusion of subjects of partly missing data.. We will complete the existing methodology by showing how our work can be extended to accommodate other types of outcome data, including continuous and countered multiple source data. We will revisit our proposed methods for non-response to show how an alternative approach may be useful. We will develop new methods for handling the analysis of data obtained in two stage designs. Finally, we will develop extensions of the methods which can be used to analyze multiple source data obtained repeatedly over time in longitudinal studies. We will develop appropriate software so that these methodological developments can be readily used in psychiatric research. We will devise macros and procedures which can be used with existing, widely available packages. We will apply our methodology to five existing data sets; four data sets come from ongoing studies which involve collaborations with this group of investigators. The proposed methods will also be directly applicable to several recent NIMH initiatives.