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: 8447584
• 负责人: M LEE VAN HORN
• 金额: $ 20.61万
• 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 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8447584
• 关键词: Achievement; Address; Adolescent; Applied Research; Awareness; Child; Communities; Complex; Data; Data Set; Development; Diagnosis; Effectiveness; Evaluation; Family; Goals; Grant; Hand; Health; Individual; Individual Differences; Intervention; Lead; Mental Depression; Methodology; Methods; Modeling; Motivation; Outcome; Performance; Physical activity; Population; Procedures; Process; Public Health; Research; Research Personnel; Respondent; Risk; Schools; Social skills development; Social support; Solutions; Specific qualifier value; Statistical Methods; Stress; Students; Techniques; Testing; Underserved Population; Validation; Variant; Work; base; contextual factors; effective therapy; experience; innovation; novel; peer; performance tests; prevent; public health relevance; response; simulation; social skills; stressor; tool

DESCRIPTION (provided by applicant): This grant will examine the use of new statistical methods for better identifying groups of respondents who differ in the effects of contexts (families, schools, and communities) on outcomes. The grant focuses on the use of regression mixture models, which have the unique ability to identify groups of subjects based on the relationship of predictors with outcomes. This work focuses on the use of these methods under conditions encountered when assessing the effects of contexts with real data. Specifically, we aim to: 1) test new methods for validating the results of regression mixtures; 2) examine the use of regression mixtures for assessing complex moderation where many contextual variables come together to cause differential effects; and 3) test the use of regression mixtures for assessing multilevel contextual effects, including developing a set of best practices for the use of these methods. These aims will be investigated through the use of Monte Carlo simulations as well as applications with three real datasets looking at the effects of families, schools, and communities on physical activity, achievement and social skills, and the development of depression. This grant aims to provide a basis for better finding differential effects, the tools developed will allow for better understanding of differences in the effects of environmental contexts which are expected to inform efforts to prevent and treat health problems.

描述（由申请人提供）：这笔赠款将研究新统计方法的使用，以更好地识别环境（家庭、学校和社区）对结果的影响不同的受访者群体。该资助重点关注回归混合模型的使用，该模型具有根据预测变量与结果的关系识别受试者组的独特能力。这项工作的重点是在评估真实数据环境影响时遇到的条件下使用这些方法。具体来说，我们的目标是：1）测试验证回归混合结果的新方法； 2）检查使用回归混合来评估复杂的调节，其中许多上下文变量聚集在一起会产生不同的影响； 3) 测试使用回归混合来评估多层次情境效应，包括开发一套使用这些方法的最佳实践。这些目标将通过使用蒙特卡罗模拟以及三个真实数据集的应用来研究，这些数据集着眼于家庭、学校和社区对身体活动、成就和社交技能以及抑郁症发展的影响。这笔赠款旨在为更好地发现差异效应提供基础，开发的工具将有助于更好地了解环境背景影响的差异，这有望为预防和治疗健康问题的努力提供信息。