Psychometric and Genetic Assessments of Substance Use

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。总 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 其统计特性。那些表现足够好的模型将被应用于一组或多组 数据，为物质使用评估带来新的见解。第一个目标是扩展模型， 阶乘不变性，这是检验组间差异的基础。主伸展 将允许测试不变性不仅在不同的群体之间，而且在群体内， 连续变量，如年龄。这种方法将被应用于验证性因素分析， 潜在类别分析，以及代表因素和潜在类别两者的混合物的模型，并且将 能够处理二进制，序数和连续观测变量。该方法应证明有价值， 评估人口中的药物滥用模式是否代表责任的连续变化， 是否存在不同的群体。第二个目标是在以下背景下扩展机制转换模型： 增长曲线和其他因素混合模型。这一目标旨在为数据提供一个更好的模型， 涉及物质使用的开始和抵消，并协助发现异质性。第三，我们将发展 分析某些形式的部分匿名数据的方法，例如涉及随机 反应将比较这些方法在检测与预测因子的关系时的性能， 部分随机数据的后遗症和相关性，包括亲属之间的相似性， 结果。所有模型开发的设计都将允许分析和利用所收集的数据 这些数据来自亲属，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。一个 将收集和分析一系列横截面、纵向和遗传信息数据集。

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, tc 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, sequelae 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.