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

描述(由申请人提供)：建议的研究项目是响应PA-10-018(利用现有的流行病学、预防和治疗研究数据加快药物滥用研究的步伐)的R01项目。该项目的目标是在预防干预试验的背景下，建立一个考虑纵向物质使用轨迹异质性的因果推断框架。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。混合生长模型是一种基于经验模型拟合的识别非均质轨迹地层和特定地层干预效果的灵活工具。然而，对于我们如何利用增长混合模型结果作为因果干预效应的有力证据，当它们的识别严重依赖于经验拟合和参数假设时，我们知之甚少。因果建模指的是一种推理框架，它侧重于澄清使因果解释成为可能的假设。这种方法的优点在于，因果推理的质量可以通过假设的科学合理性和基于这些假设的敏感性分析的质量来评估。尽管整合这两个框架有很大的潜在好处，但到目前为止，几乎没有进行过研究来检验这种可能性。拟议的项目旨在指导这一整合，并为考虑到物质使用发展的纵向异质性提供一个实用的因果推断框架。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)和约翰霍普金斯大学预防性干预研究中心研究(JHU PIRC：Ialongo等人，1999)。该项目将使用尖端生长混合建模方法对这些数据进行广泛的二次分析，特别侧重于评估具有不同物质使用轨迹的群体的干预效果。我们期望，我们的研究将促进高质量的二次分析，并通过改善对不同干预效果的评估以及 确定将从干预中获益最多的亚人群。

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.