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

背景中的风险：通过背景交互进行风险建模的新方法

• 批准号: 7320069
• 负责人: M LEE VAN HORN
• 金额: $ 11.73万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-22 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7320069
• 关键词: Address; Adult; Affect; Characteristics; Child; Class; Complex; Condition; Data; Data Set; Development; Disease regression; Early Intervention; Empirical Research; Environment; Evaluation; Event; Family; Future; Health; Horns; Individual; Individual Differences; Intention; Intervention; Joints; Knowledge; Lead; Life; Literature; Mental Depression; Methodology; Methods; Modeling; Outcome; Performance; Population; Prevention; Probability; Problem behavior; Process; Public Health; Purpose; Research; Resources; Respondent; Risk; Risk Factors; Sampling; Schools; Statistical Models; Stress; Structure; Students; Study models; System; Techniques; Testing; Thinking; Weight; Work; base; demographics; design; experience; improved; innovation; simulation; social skills; theories; tool

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.

描述（由申请人提供）：长期以来，关于情境环境如何影响健康结果的理论工作一直强调互动效应的重要性：情境通常会对个体产生不同的影响。事实上，不良发育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重风险因素的人产生负面影响。然而，对这些相互作用的研究通常仅限于研究简单的二元相互作用。本研究的目的是发展和展示一种新的统计工具的效用，随机效应回归混合模型，以进一步研究风险的背景。这些模型的工作原理是识别受环境影响的潜在个体类别，然后提供工具来理解这些儿童的差异。本研究将评估使用这些模型进行有效推断的条件，然后展示它们在评估压力生活事件的不同影响方面的应用。然后将进一步发展这些模型，使其能够通过包含随机效应而用于多层数据。将进行蒙特卡罗模拟，以评估这些模型的效用，同时对大量高风险儿童样本进行分析，以证明这些模型具有真实数据的效用。结果将集中在证明该模型的有效性时，用于研究如何发展环境影响儿童的不同。在最后一组分析中，多层随机效应模型将应用于来自第二项研究的数据，以评估压力生活事件对抑郁症发展的影响。这个应用程序对于证明以前的结果不是特定于情况的证据很重要。此外，由于第二项研究旨在通过风险相互作用来观察复杂的环境，因此该应用程序应该证明这些模型作为回答现实世界研究问题的工具的力量。