PSYCHIATRIC EPIDEMIOLOGY: LONGITUDINAL STUDY METHODOLOGY

纵向研究通常被设计为跟踪精神病学的进程 多年来的患者，以便可以描述长期结果 而且可以确定可能预测结果的因素。纵向 研究也在精神病学领域的各种环境中使用 流行病学和临床精神病学研究。事实上，纵向的 研究设计是最强大的研究工具之一 现代精神病学研究者。这项研究的目标是将 聚集在一起的一群研究人员在 纵向数据方法论及其在精神病学分析中的应用 流行病学和临床资料。总体目标是研究和 扩展当前可用的纵向数据分析方法 并开发新的参数和非参数方法来发送这种信号 纵向数据。 在精神病学流行病学领域产生的纵向数据和 临床精神病学通常充满不完整或不完整的问题 缺少观测值，测量尺度较弱(顺序或标称尺度 变量)，以及在每个对照组中观察的次数不相等。 虽然在生物识别文献中已经有了大量的进展 近年来，在加强技术方面仍有许多工作要做 可供精神病流行病学家和临床研究人员使用。至 为此，我们确定了四个具体目标，每个目标都涉及方法 用于纵向数据的分析。这些目标是：1)学习和 比较以下几种纵向数据分析方法的特性 的数据是不完整的(即，有遗漏的观测值时)； 发展半参数方法学来分析重复测量 顺序量表反应变量；3)开发拟合度程序 用于评估参数和半参数回归的充分性 纵向数据模型；4)发展非参数方法 当反应变量为非正态时，比较受试组。 将在实现这些目标的同时应用以下成果 这些方法适用于现有的精神病学数据集。特别强调的是 给出了精神病学领域出现的数据集 流行病学和正在进行的临床研究研究中 爱荷华州立大学心理健康临床研究中心主任。

Longitudinal studies are often designed to follow the course of psychiatric patients over a number of years, so that long term outcome can be described and factors that may predict outcome can be identified. Longitudinal studies are also used in a variety of settings in the fields of psychiatric epidemiology and clinical psychiatric research. Indeed, the longitudinal study design is one of the most powerful research tools available to the modern psychiatric researcher. The goal of this research is to bring together a group of researchers with experience and expertise in longitudinal data methodology and in the analysis of psychiatric epidemiologic and clinical data. The overall objective is to study and extend currently available methods for the analysis of longitudinal data and to develop new parametric and nonparametric methods for signaling such longitudinal data. Longitudinal data which arise in the field of psychiatric epidemiology and clinical psychiatry are typically fraught with problems of incomplete or missing observations, weak measurement scales (ordinal or nominal scale variables), and an unequal number of observations in each comparison group. While there have been numerous advances in the biometric literature in recent years, much work remains to be done to strengthen the techniques available to the psychiatric epidemiologist and clinical researcher. To this end, we have identified four specific aims, each dealing with methods for the analysis of longitudinal data. These aims are: 1) to study and compare the properties of methods for analyzing longitudinal data when some of the data are incomplete (i.e., when there are missing observations); 2) to develop semiparametric methodology for analyzing repeated measures of an ordinal scale response variable; 3) to develop goodness-of-fit procedures for assessing the adequacy of parametric and semi-parametric regression models for longitudinal data; 4) to develop nonparametric methods for comparing groups of subjects when the response variable is non-normal. These aims will be pursued while simultaneously applying the results of these methods to existing psychiatric data sets. Particular emphasis will be given to the data sets which have arisen in the field of psychiatric epidemiology and in the clinical research studies being conducted as part of the University of Iowa's Mental Health Clinical Research Center.