ANALYSIS OF MULTIPLE INFORMANT DATA IN PSYCHIATRY

该提案将制定方法，用于分析在旨在衡量社区和服务样本中的精神健康结果或风险因素的调查中收集的多个信息提供者和/或多个评估数据。使用多个信息提供者（父母，教师，儿童，临床医生，同龄人）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。多源数据在其他精神病学研究中也很常见（例如多个精神病学评估）。在合并多个来源的数据方面遇到的一些困难包括：来源之间在某些结果方面存在系统性差异，提供信息者的一致性水平相对较低，以及某些来源的信息缺失。从1995年发表的工作开始，我们已经开发了一种通用的方法来分析多源数据，重点是分类结果。具体来说，我们已经开发出将多源数据纳入逻辑回归框架的方法，其中结果或预测因子可能来自多个来源。这些方法适应了在一个或多个来源中缺失响应的可能性。首先，我们将开发一个通用的回归模型的情况下，多个来源被用来确定风险因素，并建立在我们以前的工作在这种情况下。该模型在概念上类似于我们最初的回归模型，其结果由多个来源测量。它将允许在简约模型拟合中使用所有可用数据，研究不同来源的影响，并纳入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来完成现有的方法，包括连续和计数的多源数据。我们将重新审视我们提出的无响应方法，以显示替代方法可能是有用的。我们将开发新的方法来处理两阶段设计中获得的数据分析。最后，我们将开发扩展的方法，可用于分析在纵向研究中随着时间的推移重复获得的多源数据。我们将开发适当的软件，使这些方法的发展可以很容易地用于精神病学研究。我们将设计宏和过程，可以与现有的，广泛使用的软件包。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，涉及与这组研究人员的合作。所提出的方法也将直接适用于最近的几个NIMH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.