Generalized Causal Mediation Analysis for Social Environment and Health Research

描述（由申请人提供）：社会环境对健康结果的影响已在许多研究中得到证实。然而，这些影响发生的机制或中间变量在很大程度上是未知的。虽然中介分析的方法存在的中间变量（或调解人）的研究，这些方法是不足以处理社会环境数据的挑战性功能。拟议的研究将开发新的中介分析方法，使用潜在的结果和结构（因果）模型框架。我们的双管齐下的方法，被称为广义因果中介分析，将提供新的方法来拟合因果模型和计算中介效应。特别是，我们将通过处理具有以下特征的模型来扩展以前的方法：1）混合离散和连续变量，2）多个暴露变量，3）异质中介效应，4）多水平数据，5）潜变量。进一步扩展因果模型框架，其中调解有一个干预的解释，我们将开发一种方法来预测一个新的干预的效果。这种方法将涉及用户增强一个合适的因果模型，从而综合理论和经验的结果。这些方法将被应用到三个激励数据集：1）家庭环境和龋齿数据从224非常低出生体重和正常出生体重的青少年和他们的父母; 2）数据从一项干预研究，以防止性病涉及1357郊区高中学生;和3）数据从一项研究的肿瘤科医生-患者的沟通和患者决策满意度的影响。将通过数据应用和模拟研究对新方法进行评估和改进。最后，该项目将开发用户友好的计算机程序，使行为和其他健康研究能够应用新方法。新的方法将允许更精细，有效和彻底的探索社会环境影响的调解途径。他们还将利用有关社会环境机制的知识，促进制定有效的行为和其他干预措施，以改善健康结果。

DESCRIPTION (provided by applicant): The effects of the social environment on health outcomes have been demonstrated in numerous research studies. However, the mechanisms or intermediate variables through which these effects occur are largely unknown. While methods for mediation analysis exist for the study of intermediate variables (or mediators), these methods are inadequate for handling the challenging features of social environment data. The proposed research will develop new mediation analysis methods using a potential outcomes and structural (causal) model framework. Our two-pronged approach, referred to as generalized causal mediation analysis, will provide new methods for both the fitting of causal models and the computation of mediation effects. In particular, we will extend previous methods by handling models with the following features: 1) mixed discrete and continuous variables, 2) multiple exposure variables, 3) heterogeneous mediation effects, 4) multi-level data, and 5) latent variables. Further extending the causal model framework, in which mediation has an intervention interpretation, we will develop a method to predicting the effect of a new intervention. This approach will involve user augmentation of a fitted causal model, thus synthesizing theory and empirical results. The methods will be applied to three motivating datasets: 1) family environment and dental caries data from 224 very low birth weight and normal birth weight adolescents and their parents; 2) data from a study of an intervention to prevent STDs involving 1357 suburban high schools students; and 3) data from a study of oncologist-patient communication and its effect on patient decision satisfaction. The new methods will be evaluated and refined through the data applications and through simulation studies. Finally, the project will develop user-friendly computer programs to allow behavioral and other health researches to apply the new methods. The new methods will allow a more refined, valid, and thorough exploration of mediating pathways for social environment effects. They will also facilitate the formulation of effective behavioral and other interventions, utilizing knowledge about social environment mechanisms, to improve health outcomes.