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: 8829685
• 负责人: M LEE VAN HORN
• 金额: $ 19.75万
• 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 2015-11-20
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8829685
• 关键词: Academic skills; Achievement; Address; Adolescent; Applied Research; Awareness; Child; Communities; Complex; Data; Data Set; Development; Diagnosis; Effectiveness; Evaluation; Family; Goals; Grant; Hand; Health; Individual; Individual Differences; Lead; Mental Depression; Methodology; Methods; Modeling; Monte Carlo Method; Motivation; Outcome; Performance; Physical activity; Population; Procedures; Process; Research; Research Personnel; Respondent; Risk; Schools; Social skills development; Social support; 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; personalized intervention; prevent; public health intervention; response; simulation; social; 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）：该赠款将研究使用新的统计方法，以更好地识别背景（家庭，学校和社区）对结果的影响的受访者组。该赠款的重点是使用回归混合模型，这些模型具有独特的能力，可以根据预测因子与结果的关系来识别受试者群体。这项工作重点是在评估具有真实数据的上下文的影响时遇到的条件下使用这些方法。具体而言，我们的目的是：1）测试新方法以验证回归混合物的结果； 2）检查回归混合物评估复杂适中的使用，其中许多上下文变量聚集在一起引起差异效应； 3）测试回归混合物评估多级上下文效果的使用，包括开发一组用于使用这些方法的最佳实践。这些目标将通过使用蒙特卡洛模拟以及三个真实数据集的应用来研究这些目标，以研究家庭，学校和社区对体育活动，成就和社交技能的影响以及抑郁症的发展。该赠款旨在为更好地寻找差异效果提供基础，开发的工具将使人们对环境环境的影响的差异更好地理解，这些环境环境的影响有望为预防和治疗健康问题提供努力。