ANALYSIS OF MULTIPLE INFORMANT DATA IN PSYCHIATRY

这项提案将制定方法，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告密者和/或多个评估数据。使用多个告密者(家长、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的信息的最佳途径。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)。在综合来自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，举报人协议程度相对较低，以及缺少某些来源的信息。从1995年发表的工作开始，我们开发了一种一般方法来分析多源数据，重点放在分类结果上。具体地说，我们开发了将多源数据合并到Logistic回归框架中的方法，在该框架中，结果或预测因素可能来自多个来源。这些方法适应了在一个或多个来源中遗漏答复的可能性，目前的提案试图进行这一研究。首先，我们将为使用多个来源确定风险因素的情况开发一个通用回归模型，该模型建立在我们在此背景下的先前工作的基础上。这个模型将在概念上类似于我们最初的回归模型，结果由多个来源衡量。它将允许在简约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受试者。我们将通过展示我们的工作如何扩展以适应其他类型的结果数据，包括连续和反多源数据，从而完成现有的方法。我们将重新讨论我们提出的无响应方法，以说明替代方法可能是如何有用的。我们将开发新的方法来处理在两阶段设计中获得的数据的分析。最后，我们将开发可用于分析在纵向研究中重复获得的多源数据的方法的扩展。我们将开发适当的软件，以便这些方法的发展可以随时用于精神病学研究。我们将设计可与现有的、广泛可用的包一起使用的宏和程序。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，涉及到与这组调查人员的合作。拟议的方法也将直接适用于最近的几项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.