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）：该补助金将研究新的统计方法的使用，以更好地识别在环境（家庭，学校和社区）对结果的影响方面存在差异的受访者群体。该补助金的重点是使用回归混合模型，该模型具有独特的能力，可以根据预测因素与结果的关系来识别受试者组。这项工作的重点是使用这些方法时遇到的条件下，评估上下文与真实的数据的影响。具体而言，我们的目标是：1）测试验证回归混合结果的新方法; 2）检查回归混合用于评估复杂适度的使用，其中许多上下文变量聚集在一起导致差异效应; 3）测试回归混合用于评估多层次上下文效应的使用，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洛模拟以及三个真实的数据集的应用程序进行调查，这些数据集关注家庭，学校和社区对身体活动，成就和社交技能以及抑郁症的发展的影响。该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，预计这些差异将为预防和治疗健康问题的努力提供信息。