Psychometric and Genetic Assessments of Substance Use

描述（由申请人提供）： 该项目旨在开发一系列新的方法来对药物使用和滥用进行表型分析。一般计划是从理论上开发统计模型，在用户友好的软件中实现它们，并检查它们的统计特性。那些表现足够好的模型将被应用于一组或多组数据，以便为物质使用评估带来新的见解。第一个目标是扩展阶乘不变性模型，这是检验组间差异的基础。主要的扩展将允许测试的不变性不仅在不同的群体之间，但也在群体内的连续变量，如年龄。这种方法将被应用于验证性因素分析，潜在类分析，并代表混合物的因素和潜在类的模型，并将能够处理二进制，有序和连续观测变量。该方法应证明有价值的评估是否在人口中的药物滥用模式代表连续变化的责任或是否存在不同的群体。第二个目标是在增长曲线和其他因素混合模型的背景下，扩大制度转换的模型。这一目的旨在为涉及药物使用起始和抵消的数据提供更好的模型，并协助揭示异质性。第三，我们将开发用于分析某些形式的部分匿名数据的方法，例如涉及随机应答的数据。将比较这些方法在检测与部分随机数据的预测因子、因果关系和相关性（包括亲属和结局之间的相似性）的关系方面的性能。所有模型开发的设计都将允许分析和利用从亲属那里收集的数据，并将包括遗传标记数据模型，用于连锁和关联研究。应用数据分析将产生实质性结果，指导模型开发，并测试稳健性。将收集和分析一系列横截面、纵向和遗传信息数据集。

DESCRIPTION (provided by applicant): This project aims to develop a series of novel approaches to phenotyping drug use and abuse. The general scheme is to develop statistical models from theory, implement them in user friendly software, and examine their statistical properties. Those models that perform sufficiently well will be applied to one or more sets of data to bring new insight into the assessment of substance use. The first goal is to extend of models for factorial invariance, which form the basis of testing for differences between groups. The primary extension will be to allow testing of invariance not merely between distinct groups, but also within groups that vary with respect to continuous variables such as age. This approach will be applied to confirmatory factor analysis, to latent class analysis, and to models that represent mixtures of both factors and latent classes, and will be able to handle binary, ordinal and continuous observed variables. The method should prove valuable in assessing whether substance abuse patterns in the population represent continuous variation in liability or whether distinct groups exist. The second goal is to extend models for regime switching in the context of growth curve and other factor mixture models. This aim is intended to provide a better model for data that involve onset and offset of substance use, and to assist in uncovering heterogeneity. Third, we will develop methods for the analysis of certain forms of partially anonymized data such as those involving randomized response. These methods will be compared for their performance at detecting relationships with predictors, sequellae and correlates of partially randomized data, including resemblance between relatives and outcomes. All model development will be designed to permit the analysis and exploitation of data collected from relatives, and will include models for data on genetic markers, for both linkage and association studies. Applied data analyses will yield substantive results, guide model development, and test for robustness. An array of cross-sectional, longitudinal, and genetically informative datasets will be assembled and analyzed.