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

描述(由申请者提供)：这项资助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。这笔赠款的重点是回归混合模型的使用，该模型具有根据预测因素与结果的关系确定受试组的独特能力。这项工作侧重于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。具体地说，我们的目标是：1)测试验证回归混合结果的新方法；2)检查回归混合用于评估复杂缓和的使用，其中许多上下文变量聚集在一起导致不同的影响；以及3)测试回归混合用于评估多级别上下文影响的使用，包括为使用这些方法开发一套最佳实践。这些目标将通过使用蒙特卡罗模拟以及使用三个真实数据集的应用程序来调查，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。这笔赠款旨在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影响的差异，这些环境背景有望为预防和治疗健康问题的努力提供信息。

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.