Marginal Causal Models for Observational Studies in Obesity Research

肥胖研究观察研究的边际因果模型

• 批准号: 7898618
• 负责人: Donna Coffman
• 金额: $ 14.49万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-20 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7898618
• 关键词: Adolescence; Behavior; Behavior Therapy; Body Weight; Characteristics; Complex; Computer software; Data; Data Set; Diagnostic Errors; Diet; Drug Formulations; Environment; Epidemiology; Estimation Techniques; Individual; Methodology; Methods; Modeling; Obesity; Observational Study; Outcome; Overweight; Physical activity; Probability; Randomized; Randomized Controlled Trials; Research; Research Personnel; Robin bird; Statistical Methods; Structural Models; Surveys; Techniques; Variant; Weight; base; design; effective intervention; obesity prevention; public health relevance; restraint; tool; user friendly software; young adult

DESCRIPTION (provided by applicant): There are many research questions about effects of naturally0ccuring behaviors (e.g. dieting) on overweight and obesity for which a randomized controlled trial would be untenable. For these we must rely on observational studies, and causal inference with observational data poses difficult methodological challenges. Many techniques have been proposed for estimating average causal effects, but tools for modeling variation in these effects are scarce. In this R21 project, we will develop a new methodology for causal regression which allows effects at the individual level to covary with characteristics of individuals, contexts and environments. Our formulation is similar to that of the marginal structural model developed by James Robins et al., but we propose a new estimation technique based on imputation rather than weighting. We will obtain estimates and standard errors, diagnostics to help users identify shortcomings in the model, and methods to handle data from surveys with complex design features (strata, clusters, unequal probabilities of selection). Methods will be implemented in a user-friendly software and made available to health-outcome researchers. Secondary analyses will be performed on three epidemiologic datasets to assess the variation in effects dietary restraint, physical activity and other behaviors on body weight and other sequelae in adolescence and young adulthood. PUBLIC HEALTH RELEVANCE: This project will (a) generate new statistical methods and software to help obesity researchers draw robust conclusions about effects of behaviors and treatments that have not been randomized, and (b) apply these methods in secondary analyses of observational data on weight-related behaviors in adolescence and young adulthood. Methods and findings will inform the design and implementation of more effective interventions for treatment and prevention of obesity.

描述（由申请人提供）：有许多关于自然行为（如节食）对超重和肥胖的影响的研究问题，随机对照试验是站不住脚的。 对于这些，我们必须依赖于观察性研究，与观察性数据的因果推理提出了困难的方法学挑战。 已经提出了许多技术来估计平均因果效应，但在这些影响的建模变化的工具是稀缺的。在这个R21项目中，我们将开发一种新的因果回归方法，使个体水平的影响与个体，背景和环境的特征协变。我们的公式类似于James Robins等人开发的边际结构模型，但我们提出了一种新的估计技术的基础上填补，而不是加权。我们将获得估计值和标准误差，诊断以帮助用户识别模型中的缺点，以及处理具有复杂设计特征（分层，聚类，不平等选择概率）的调查数据的方法。方法将在一个用户友好的软件中实施，并提供给健康结果研究人员。将对三个流行病学数据集进行二次分析，以评估饮食限制、体力活动和其他行为对青春期和青年期体重和其他后遗症的影响。公共卫生关系：该项目将（a）产生新的统计方法和软件，以帮助肥胖研究人员得出关于未随机化的行为和治疗效果的可靠结论，以及（B）将这些方法应用于对青春期和青年期体重相关行为的观察数据的二次分析。方法和研究结果将为设计和实施更有效的干预措施以治疗和预防肥胖提供信息。