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: 8105876
• 负责人: M LEE VAN HORN
• 金额: $ 21.72万
• 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/8105876
• 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; 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. PUBLIC HEALTH RELEVANCE: This proposal aims to develop and test statistical tools to allow researchers to better understanding individual differences in the effects of contexts. This has important implications for creating public health interventions which are tailored to the specific needs of individuals, allowing for the targeting of those who are most likely to benefit.

描述（由申请人提供）：这项资助将研究新的统计方法的使用，以更好地识别背景（家庭、学校和社区）对结果影响不同的受访者群体。该基金的重点是使用回归混合模型，该模型具有独特的能力，可以根据预测因子与结果的关系来识别受试者群体。这项工作的重点是在使用真实数据评估上下文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适度，其中许多上下文变量聚集在一起导致差异效应；3)测试使用回归混合来评估多层次上下文效应，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程序进行调查，这些数据集着眼于家庭，学校和社区对体育活动，成就和社交技能以及抑郁症发展的影响。这笔赠款的目的是为更好地发现不同的影响提供基础，所开发的工具将使人们能够更好地了解环境背景的不同影响，这些影响有望为预防和治疗健康问题的努力提供信息。