Continuous Time Causal Mediation Models for Social Behavior in Health

健康研究越来越多地集中在发现一种机制上， 接触或干预会影响健康结果。社会科学已经成为这幅图景的一部分， 社会行为和其他社会因素在健康方面的重要性日益得到承认。 因果中介分析方法已被开发来探索这种机制。然而，在这方面， 这些方法都是建立在离散时间模型的基础上的，没有考虑到连续时间的本质 许多，如果不是大多数，社会和行为，以及生物，过程。拟议的研究将 开发一个新的连续时间因果中介分析模型（CMM）。除了允许A 新模型描述了变量之间的动态相互作用， 连续时间特征，如时滞和消光效应。该模型将容纳 任意数据模式、多重因果关系、连续变量和分类变量，以及 相互作用/修饰效应。我们从基于势的微分方程模型开始 结果（因果）框架，并显示与可能适合的非线性模型形式的联系 数据整合方法可用于估计中介/路径效应并预测 新的干预措施。其次，我们将扩展CMM来处理多级数据，例如，家庭， 邻里或其他社会变量，这些变量本身可能会随着时间的推移而展开。此外，我们将 建立个体之间相互影响的模型，为突发性社会行为提供解释。 我们将把我们的新方法应用于一项家庭干预的随机研究，以改善牙齿护理 在儿童中的使用和对家庭、邻里、行为和生物学的纵向观察研究 弱势群体和高危人群（包括极低出生率人群）龋齿进展的因素 体重）的孩子。将通过模拟研究对新方法进行评估和改进。新 连续时间调解模型将进一步用于研究设计问题，包括数量和 测量的时间安排，以及研究者的新设计，例如随机分配不同的受试者 不同的测量时间。最后，我们将开发用户友好的计算机程序， 行为和其他健康研究人员应用新方法。新方法将允许更多的 有效和彻底的探索涉及社会行为的因果机制，具有广泛的适用性 在heath地区。他们还将促进有效的行为和其他发展 采取干预措施，改善健康成果，减少健康差距。

Health research has been increasingly focused on discovering the mechanisms through which an exposure or intervention affects health outcomes. Social science has become a part of this picture as the importance of social behavior and other social factors in health has been increasingly recognized. Causal mediation analysis methods have been developed to explore such mechanisms. However, these approaches are rooted in discrete time models, and fail to account for the continuous time nature of many, if not most, social and behavioral, as well as biological, processes. The proposed research will develop a new continuous time causal mediation analysis model (CMM). In addition to allowing a description of the dynamic interplay among variables in time, the new model will allow the incorporation of continuous time features such as time lags and extinction effects. The model will accommodate arbitrary data patterns, multiple causal links, continuous and categorical variables, and interaction/modification effects. We start with a differential equation model based on a potential outcome (causal) framework, and show the connection to a nonlinear model form that may be fit to data. An integration approach may be used to estimate mediation/path effects and predict the effect of new interventions. Secondly, we will extend CMMs to handle multi-level data, for example, family, neighborhood or other social variables, which may themselves unfold over time. In addition, we will model the impact of individuals on one another, providing a explanation for emergent social behavior. We will apply our new methods to a randomized study of family interventions to improve dental care use in children and a longitudinal observational study of family, neighborhood, behavioral and biologic factors in the progression of dental caries in disadvantaged and high risk (including very low birth weight) children. The new methods will be evaluated and refined through simulation studies. The new continuous time mediation models will be further used to study design issues, including the number and timing of measurements, and to investigator new designs, for example that randomize different subjects to different measurement times. Finally, we will develop user-friendly computer programs to allow behavioral and other health researchers to apply the new methods. The new methods will allow a more valid and thorough exploration of causal mechanisms involving social behavior, with broad applicability across heath areas. They will also facilitate the development of effective behavioral and other interventions to improve health outcomes and decrease health disparities.