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确定药物使用和滥用的表型。一般的方案是从理论上建立统计模型，在用户友好的软件中实现它们，并检查它们的统计性质。那些表现得足够好的模型将被应用于一组或多组数据，以对物质使用情况的评估带来新的见解。第一个目标是扩展阶乘不变性的模型，这构成了测试组之间差异的基础。主要的扩展将是不仅允许在不同的组之间测试不变性，而且还允许在相对于年龄等连续变量变化的组内测试不变性。这一方法将应用于验证性因素分析、潜在类别分析以及代表因素和潜在类别混合的模型，并将能够处理二元、有序和连续的观测变量。该方法在评估人群中的药物滥用模式是否代表责任的持续变化或是否存在不同的群体方面应被证明是有价值的。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展制度转换模型。这一目标旨在为涉及药物使用开始和抵消的数据提供一个更好的模型，并帮助揭示异质性。第三，我们将开发分析某些形式的部分匿名数据的方法，例如那些涉及随机响应的数据。将比较这些方法在检测与部分随机数据的预测因素、后遗症和相关性方面的性能，包括亲属和结果之间的相似性。所有模型开发的目的都是为了能够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的模型，用于连锁和关联研究。应用的数据分析将产生实质性的结果，指导模型开发，并测试稳健性。一组横截面的、纵向的和遗传信息丰富的数据集将被组装和分析。

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.