Heterogeneity in Prevention Intervention Effects On Substance Use: A Latent Varia

预防干预对药物使用影响的异质性：潜在变量

• 批准号: 8457018
• 负责人: BOOIL JO
• 金额: $ 20.68万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-15 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8457018
• 关键词: Accounting; Alcohol or Other Drugs use; Data; Data Analyses; Development; Drug abuse; Early Intervention; Epidemiology; Evaluation; Future; Goals; Growth; Heterogeneity; Hybrids; Intervention; Intervention Studies; Intervention Trial; Investigation; Joints; Lead; Methods; Modeling; One-Step dentin bonding system; Prevention; Preventive; Preventive Intervention; Public Health; Research; Research Design; Research Project Grants; Rest; Science; Universities; adolescent substance abuse; base; design; flexibility; improved; intervention effect; response; substance abuse prevention; tool; treatment effect

DESCRIPTION (provided by applicant): The proposed research project is an R01 project in response to PA-10-018 (Accelerating the pace of drug abuse research using existing epidemiology, prevention, and treatment research data). The goal of this project is to establish a framework for causal inference accounting for heterogeneity in longitudinal substance use trajectories in the context of prevention intervention trials. To accomplish this goal, we propose to integrate two powerful modeling frameworks, growth mixture modeling and causal modeling. Growth mixture modeling is a flexible tool for identifying heterogeneous trajectory strata and strata-specific intervention effects based on empirical model fitting. However, little is known about how we can use the growth mixture modeling results as strong evidence of causal intervention effects when their identification heavily relies on empirical fitting and parametric assumptions. Causal modeling refers to an inferential framework that focuses on clarification of assumptions that make causal interpretation possible. The strength of this approach is that the quality of causal inference can be evaluated by the scientific plausibility of the assumptions and the quality of sensitivity analysis based on these assumptions. Despite the significant potential benefit of integrating the two frameworks, little research has been conducted so far to examine such possibility. The proposed project is intended to guide this integration and to provide a practical framework for causal inference accounting for longitudinal heterogeneity in substance use development. Our investigations will be guided by existing data from two intervention studies: Adolescent Substance Abuse Prevention Study (AS- APS: Sloboda et al., 2009) and Johns Hopkins University Preventive Intervention Research Center Study (JHU PIRC: Ialongo et al., 1999). This project will provide extensive secondary analyses of these data using cutting-edge growth mixture modeling methods, in particular focusing on estimating intervention effects among groups with heterogeneous substance use trajectories. We expect that our study will promote high quality secondary analysis and improve the design of future substance use intervention trials by improving the evaluation of differential intervention effects as well as the identification of subpopulations who would benefit most from the intervention.

描述（由申请人提供）：拟议的研究项目是响应 PA-10-018（利用现有流行病学、预防和治疗研究数据加快药物滥用研究步伐）的 R01 项目。该项目的目标是建立一个因果推理框架，以解释预防干预试验背景下纵向物质使用轨迹的异质性。为了实现这一目标，我们建议集成两个强大的建模框架：增长混合建模和因果建模。增长混合模型是一种灵活的工具，用于基于经验模型拟合来识别异质轨迹层和特定层的干预效果。然而，当其识别严重依赖于经验拟合和参数假设时，我们如何使用增长混合模型结果作为因果干预效应的有力证据却知之甚少。因果建模是指一种推理框架，其重点是澄清使因果解释成为可能的假设。这种方法的优点在于，可以通过假设的科学合理性以及基于这些假设的敏感性分析的质量来评估因果推理的质量。尽管整合这两个框架具有巨大的潜在好处，但迄今为止很少有研究来检验这种可能性。拟议的项目旨在指导这种整合，并为物质使用发展中纵向异质性的因果推断提供实用框架。我们的调查将以两项干预研究的现有数据为指导：青少年药物滥用预防研究（AS-APS：Sloboda 等人，2009 年）和约翰·霍普金斯大学预防干预研究中心研究（JHU PIRC：Ialongo 等人，1999 年）。该项目将使用尖端的增长混合建模方法对这些数据进行广泛的二次分析，特别侧重于估计具有异质物质使用轨迹的群体之间的干预效果。我们期望我们的研究将通过改进差异干预效果的评估以及 确定将从干预中受益最多的亚人群。