Model Assessment and Selection for Latent Transition Models

潜在转变模型的模型评估和选择

• 批准号: 7130112
• 负责人: Hwan Chung
• 金额: $ 7.42万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-26 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7130112
• 关键词: drug abuse; model

DESCRIPTION (provided by Investigator): Substance use is a well-known risk factor for public health and well-being in the United States. The identification of particular stages of substance use can reveal optimal opportunities for intervening in the onset process for different groups of individuals. Moreover, this information can be used to assess the relative treatment effects on different stages of substance use. Latent transition analysis (LTA) is ideally suited to this type of research, and it has been used successfully to describe stages in the area of substance use prevention and treatment. It is important that a model be assessed adequately at the outset of an analysis, as it has important ramifications for all following analysis. Drug abuse researchers currently have several methods at their disposal for evaluating the fit of LTA models, depending on the software package being used. Unfortunately, it is not clear which of the currently available methods is optimal; in fact, different available tools often suggest different models. The overall goal of this project is to evaluate and compare the performance of existing model assessment tools including new methods that are not readily available to social and behavioral scientists. The proposed project involves extensive simulations to investigate the behavior of statistical selection methods. Statistical simulations are an extremely useful tool for this type of methodological research because the correct latent class structure of the empirical data is typically unknown. We will conduct a thorough simulation study to compare ten selection methods to assess their performance under a variety of situations that occurs in practice in drug abuse intervention research. In addition, we will lay the groundwork for the development of new goodness-of-fit indices for LTA models, measuring how much better a model fits, with a 0-1 scale, as compared to a competitive alternative. As a result of this project, we will provide drug abuse scientists with methodologically sound and practical guidelines for diagnosing model fit and selecting appropriate LTA models under different conditions. A series of articles will be submitted to peer-review journals, and a project web site also will be developed in The Methodology Center web site at Penn State, where we will post information on model selection that will be accessible to drug abuse researchers.

描述（由研究者提供）：物质使用是美国公众健康和福祉的一个众所周知的风险因素。确定物质使用的特定阶段可以揭示干预不同群体个体发病过程的最佳机会。此外，这些信息可用于评估药物使用不同阶段的相对治疗效果。潜在转变分析（LTA）非常适合这种类型的研究，它已被成功地用于描述物质使用预防和治疗领域的阶段。在分析开始时充分评估模型是很重要的，因为它对所有后续分析都有重要的影响。药物滥用研究人员目前有几种方法可用于评估LTA模型的拟合性，具体取决于所使用的软件包。不幸的是，目前尚不清楚哪一种可用的方法是最佳的；事实上，不同的可用工具通常意味着不同的模型。该项目的总体目标是评估和比较现有模型评估工具的性能，包括社会和行为科学家不易获得的新方法。拟议的项目涉及广泛的模拟，以调查统计选择方法的行为。统计模拟是这类方法论研究的一个非常有用的工具，因为经验数据的正确潜在类结构通常是未知的。我们将进行全面的模拟研究，比较十种选择方法，以评估它们在药物滥用干预研究实践中发生的各种情况下的表现。此外，我们将为LTA模型的新拟合优度指数的开发奠定基础，以0-1的尺度衡量模型与竞争替代方案相比的拟合程度。通过这个项目，我们将为药物滥用科学家在不同条件下诊断模型拟合和选择合适的LTA模型提供方法上合理和实用的指导。一系列的文章将被提交给同行评议期刊，一个项目网站也将在宾夕法尼亚州立大学方法论中心的网站上开发，我们将在那里发布关于模型选择的信息，这些信息将供药物滥用研究人员访问。