Strengthening Causal Inference in Behavioral Obesity Research

加强行为肥胖研究中的因果推断

• 批准号: 9651880
• 负责人: DAVID B ALLISON
• 金额: $ 14.14万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-20 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9651880
• 关键词: Strengthening; Causal; Inference; Behavioral; Obesity

DESCRIPTION (provided by applicant): The identification of causal relations is fundamental to a science of intervention and prevention. Obesity is a major problem for which much progress in understanding, treatment, and prevention remains to be made. Behavior is a vital component contributing to variations in energy balance and body composition, the final common pathways of obesity. Social factors are key influences on behaviors, and perhaps even physiological factors, which affect energy balance. Understanding which social and behavioral factors cause variations in adiposity and which other factors (e.g., environmental) cause variations in behavioral and social factors is vital to producing, evaluating, and selecting among intervention and prevention strategies as well as to understanding obesity's root causes. Evidence for causation (or lack thereof) of hypothesized influential factors exists on a continuum from weakest to strongest. Yet, most dialogue and research in obesity does not consider the evidence continuum between ordinary association studies (observational non-intervention studies among unrelated individuals), which do not offer strong assessments of causal effects, and randomized controlled trials (RCTs), which do offer strong inferences, but cannot be done in all circumstances. In contrast to this polarized view, there are techniques that lie intermediar between ordinary association tests and RCTs, including but not limited to quasi-experimental studies and natural experiments. Such designs are increasingly used, especially in the disciplines of economics and genetics, but are rarely used in obesity research. Our ability to draw causal inferences in obesity research could be strengthened by increased judicious use of such approaches. In-depth understanding and appropriate use of the full continuum of these methods requires input from disciplines including statistics, economics, psychology, epidemiology, mathematics, philosophy, and in some cases behavioral or statistical genetics. The application of these techniques, however, does not involve routine well-known 'cookbook' approaches but requires understanding of underlying principles, so the investigator can tailor approaches to specific and varying situations. Yet, no ongoing resource exists to provide such training and role models of scientists who regularly can and do traverse these disciplines are in short supply. The proposed annual 5-day short course on methods for causal inference in obesity research features some of the world's finest scientists who will help to fill this unmet need. This course for established and up- and-coming obesity researchers will be held annually at the University of Alabama at Birmingham. The nine course modules are formatted to provide rigorous exposure to the key fundamental principles underlying a broad array of techniques and experience in applying those principles and techniques through guided discussion of real examples in obesity research. The NIH and the scientific community at large call for better assessment of causal effect in obesity research and more training on such methods. We request the opportunity to be part of the solution.

描述（由申请人提供）：因果关系的识别是干预和预防科学的基础。肥胖是一个主要问题，在理解，治疗和预防方面取得了很大进展。行为是导致能量平衡和身体成分变化的重要组成部分，即肥胖的最终途径。社会因素是对行为甚至生理因素的关键影响，影响能量平衡。了解哪些社会和行为因素会导致肥胖的差异以及哪些其他因素（例如环境）导致行为和社会因素的差异对于在干预和预防策略中产生，评估和选择以及了解肥胖的根本原因至关重要。从最弱到最强的连续体中存在因果关系的证据（或缺乏）的证据。然而，大多数肥胖中的对话和研究都不考虑普通关联研究（无关的个体之间的观察性非干预研究）之间的证据，这些证据不提供对因果效应的强烈评估，以及随机对照试验（RCT）（RCT），这些试验（RCT）提供了强有力的推论，但在所有情况下都不能进行。与这种两极分化的观点相反，在普通关联测试和RCT之间存在中间的技术，包括但不限于准实验研究和自然实验。这种设计越来越多地使用，尤其是在经济学和遗传学的学科中，但很少在肥胖研究中使用。通过增加对这种方法的明智使用，我们可以在肥胖研究中提出因果推断的能力。深入理解和适当使用这些方法的全面连续性需要学科的意见，包括统计，经济学，心理学，流行病学，数学，哲学，以及在某些情况下行为或统计遗传学。但是，这些技术的应用并不涉及常规的众所周知的“食谱”方法，而需要了解基本原则，因此研究者可以针对特定和不同的情况定制方法。然而，尚无持续的资源来提供经常可以并且确实穿越这些学科的科学家的培训和榜样。拟议的每年5天的短期课程涉及肥胖研究的因果推断方法，其中一些世界上最好的科学家将有助于满足这种未满足的需求。这项针对既定肥胖的肥胖研究人员的课程将每年在伯明翰的阿拉巴马大学举行。九个课程模块的格式化是为了通过对肥胖研究中的真实实例进行指导讨论，在应用这些原理和技术方面进行了广泛的技术和经验的严格接触。 NIH和科学界的大规模呼吁在肥胖研究中更好地评估因果关系，并在此类方法上进行更多培训。我们要求有机会成为解决方案的一部分。