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.
Obesity affects over one-third of the US population generating a vital need for novel interdisciplinary strategies to identify factors causing (not merely correlating with) obesity and methods which cause reductions in obesity. A burgeoning array of research techniques exists to help scientists make more informed conclusions about causal effects, but many obesity researchers are unfamiliar with these techniques. The proposed course will train scientists in understanding and using such techniques, who in turn will be better positioned to help identify ways of reducing the burden of obesity.
|Dickinson, Stephanie L; Brown, Andrew W; Mehta, Tapan et al. (2018) Incorrect analyses were used in ""Different enteral nutrition formulas have no effect on glucose homeostasis but on diet-induced thermogenesis in critically ill medical patients: a randomized controlled trial"" and corrected analyses are requested. Eur J Clin Nutr :|
|McComb, Bryan; Frazier-Wood, Alexis C; Dawson, John et al. (2018) Drawing conclusions from within-group comparisons and selected subsets of data leads to unsubstantiated conclusions: Letter regarding Malakellis et al. Aust N Z J Public Health 42:214|
|Hannon, Bridget A; Thomas, Diana M; Siu, Cynthia et al. (2018) The claim that effectiveness has been demonstrated in the Parenting, Eating and Activity for Child Health (PEACH) childhood obesity intervention is unsubstantiated by the data. Br J Nutr 120:958-959|
|Brown, Andrew W; Kaiser, Kathryn A; Allison, David B (2018) Issues with data and analyses: Errors, underlying themes, and potential solutions. Proc Natl Acad Sci U S A 115:2563-2570|
|Ejima, K; Pavela, G; Li, P et al. (2018) Generalized lambda distribution for flexibly testing differences beyond the mean in the distribution of a dependent variable such as body mass index. Int J Obes (Lond) 42:930-933|
|Allison, David B (2018) The Conclusions Are Unsupported by the Data, Are Based on Invalid Analyses, Are Incorrect, and Should be Corrected: Letter Regarding ""Sleep Quality and Body Composition Variations in Obese Male Adults after 14 weeks of Yoga Intervention: A Randomized Con Int J Yoga 11:83-84|
|Dawson, J A; Brown, A W; Allison, D B (2018) The stated conclusions are contradicted by the data, based on inappropriate statistics, and should be corrected: comment on 'intervention for childhood obesity based on parents only or parents and child compared with follow-up alone'. Pediatr Obes 13:656-657|
|Speakman, J R; Loos, R J F; O'Rahilly, S et al. (2018) GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity. Int J Obes (Lond) 42:1524-1531|
|Smith Jr, D L; Thomas, D M; Siu, C O et al. (2018) Regression to the mean, apparent data errors and biologically extraordinary results: letter regarding 'changes in telomere length 3-5 years after gastric bypass surgery'. Int J Obes (Lond) 42:949-950|
|George, Brandon J; Li, Peng; Lieberman, Harris R et al. (2018) Randomization to randomization probability: Estimating treatment effects under actual conditions of use. Psychol Methods 23:337-350|
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