Risk in Context: New Methodology for Modeling Risk by Context Interactions

描述(由申请人提供)：关于背景环境如何影响健康结果的理论工作长期以来一直强调交互影响的重要性：背景通常被期望以不同的方式影响个人。事实上，不良发展结果的风险通常被认为是一种相互作用--风险因素可能只对那些经历高水平压力或多种风险因素的人产生负面影响。然而，对这些交互作用的研究通常仅限于研究简单的双变量交互作用。本研究的目的是开发和论证一种新的统计工具--随机效应回归混合模型的实用性，以进一步研究背景下的风险。这些模型的工作原理是确定潜在的受环境影响的个体类别，然后提供工具来理解这些儿童的不同之处。这项研究将评估使用这些模型做出有效推断的条件，然后演示它们在评估应激生活事件的不同影响方面的用途。然后将进一步开发这些模型，以便通过纳入随机效应将其用于多水平数据。将进行蒙特卡洛模拟，结合对高危儿童的大样本进行的分析，评估模型的实用性，以实际数据演示模型的实用性。结果将集中于证明该模型的有效性，当被用于研究发展环境如何不同地影响儿童时。在最后一组分析中，多水平随机效应模型将应用于第二项研究的数据，以评估应激性生活事件对抑郁症发展的影响。这个应用程序对于提供以前的结果不是特定于具体情况的证据很重要。此外，由于第二项研究旨在通过风险相互作用来考察复杂的背景，这一应用程序应该展示这些模型作为回答现实世界研究问题的工具的力量。

DESCRIPTION (provided by applicant): Theoretical work on how contextual environments affect health outcomes has long stressed the importance of interactive effects: contexts are often expected to affect individuals differently. In fact, risk for poor developmental outcomes is often conceived of as an interaction - risk factors may have negative effects only for those experiencing high levels of stress or multiple risk factors. Research examining these interactions, however, has typically been limited to studying simple bivariate interactions. The purpose of this study is to develop and demonstrate the utility of a new statistical tool, random effect regression mixture models, to further the study of risk in context. These models work by identifying latent classes of individuals who are differentially affected by their contexts and then provide the tools to understand what differentiates these children. The study will evaluate the conditions under which valid inferences can be made using these models and then demonstrates their use to assess differential effects of stressful life events. These models will then be further developed to allow their use with multilevel data through the inclusion of random effects. Monte Carlo simulations will be conducted to evaluate the utility of the models in conjunction with analyses conducted on a large sample of high-risk children to demonstrate the utility of the models with real data. The results will focus on demonstrating the validity of this model when used to study how developmental contexts affect children differently. In a final set of analyses multilevel random effects models will be applied to data from a second study to assess the effects of stressful life events on the development of depression. This application is important for providing evidence that previous results are not situation specific. Further, as the second study was designed to look at complex context by risk interactions this application should demonstrate the power of these models as a tool for answering real world reseach questions.