Strengthening Causal Inference in Behavioral Obesity Research

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。肥胖是一个重大问题，在认识、治疗和预防方面仍需取得很大进展。行为是导致能量平衡和身体成分变化的重要因素，这是肥胖的最终常见途径。社会因素是影响行为的关键因素，甚至可能是影响能量平衡的生理因素。了解哪些社会和行为因素导致肥胖的差异，以及哪些其他因素(如环境)导致行为和社会因素的差异，对于产生、评估和选择干预和预防策略以及了解肥胖的根本原因至关重要。假设的影响因素的因果关系(或缺乏因果关系)的证据存在于从最弱到最强的连续统一体中。然而，大多数关于肥胖的对话和研究并没有考虑到普通关联研究(在无关个体中的观察性非干预研究)和随机对照试验(RCT)之间的证据连续体，前者不能对因果效应提供强有力的评估，后者确实提供了强有力的推论，但不是在所有情况下都能完成。与这种两极分化的观点相反，有一些技术介于普通联想测试和随机对照试验之间，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被使用，特别是在经济学和遗传学领域，但很少用于肥胖症研究。我们在肥胖研究中得出因果推理的能力可以通过更多地明智地使用这种方法来增强。深入理解和适当地使用这些方法的全部连续体，需要统计学、经济学、心理学、流行病学、数学、哲学等学科的投入，在某些情况下还需要行为或统计遗传学的投入。然而，这些技术的应用并不涉及常规的众所周知的“食谱”方法，而是需要理解潜在的原则，因此调查人员可以根据具体和不同的情况定制方法。然而，没有持续的资源来提供这样的培训，经常能够并确实跨越这些学科的科学家的榜样也短缺。拟议的为期5天的关于肥胖研究中因果推断方法的年度短期课程，将以一些世界上最优秀的科学家为特色，他们将帮助满足这一未得到满足的需求。这门课程将在阿拉巴马大学伯明翰分校每年举办一次，面向已有的和未来的肥胖研究人员。九个课程模块的格式是为了通过对肥胖研究中真实例子的指导讨论，严格暴露在一系列广泛的技术和应用这些原则和技术的经验之下的关键基本原则。美国国立卫生研究院和整个科学界呼吁在肥胖研究中更好地评估因果关系，并对这种方法进行更多培训。我们要求有机会参与解决方案。

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.