The mathematical sciences including engineering, statistics, computer science, physics, econometrics, psychometrics, epidemiology, and mathematics qua mathematics are increasingly being applied to advance our understanding of the causes, consequences, and alleviation of obesity. These applications do not merely involve routine well-established approaches easily implemented in widely available commercial software. Rather, they increasingly involve computationally demanding tasks, use and in some cases development of novel analytic methods and software, new derivations, computer simulations, and unprecedented interdigitation of two or more existing techniques. Such advances at the interface of the mathematical sciences and obesity research require bilateral training and exposure for investigators in both disciplines. 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 at most institutions. The proposed annual five day short course on the mathematical sciences in obesity research features some of the world's finest scientists working in this domain to fill this unmet need by providing nine topic driven modules designed to bridge the disciplines. Each module will begin with a mathematical method applied in obesity research. This introduction will be followed by a lecture on a completed application of the method. Directly after the completed application, participants will be engaged in a guided interactive session performing calculations using software or analysis pertaining to the module topic. Finally, the module closes with a lecture on remaining open questions. In recognition of the challenges in successfully managing an interdisciplinary career, each day of the short course will contain a two hour long interactive working session guided by established senior researchers in building the next step. Participants may choose to use this interactive session to begin develop an abstract for submission at national level obesity conferences or plan an application for a small interdisciplinary group of researchers to advance their ideas at the National Institute of Mathematical and Biological Synthesis in Knoxville, TN. Based on the degree of completion, ten participants will be selected to present their work on the last day of the short course. The NIH and the scientific community at large has voiced the need for more training at the interface of the mathematical science and key biomedical domains, and we request the opportunity to be part of the solution.
Obesity affects more than one-third of the US population generating a need for novel interdisciplinary strategies to resolve growing obesity related negative health outcomes. Advanced mathematical methods play a critical role on numerous fronts in obesity research from evaluating the effect of population wide obesity prevention policy measures to monitoring patients during lifestyle interventions. The proposed course will develop connections between mathematical scientists and obesity researchers to address research challenges with novel approaches.
|Ejima, K; Pavela, G; Li, P et al. (2017) 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) :|
|Allison, D B; Thomas, D M (2017) Stated conclusion about industry funding is opposite to what the paper's data show: letter regarding 'Selective outcome reporting in obesity clinical trials: a cross-sectional review'. Clin Obes 7:402|
|Locher, Julie L; Goldsby, TaShauna U; Goss, Amy M et al. (2016) Calorie restriction in overweight older adults: Do benefits exceed potential risks? Exp Gerontol 86:4-13|
|George, Brandon J; Beasley, T Mark; Brown, Andrew W et al. (2016) Common scientific and statistical errors in obesity research. Obesity (Silver Spring) 24:781-90|
|Goldsby, TaShauna U; George, Brandon J; Yeager, Valerie A et al. (2016) Urban Park Development and Pediatric Obesity Rates: A Quasi-Experiment Using Electronic Health Record Data. Int J Environ Res Public Health 13:411|
|Dimova, Rositsa B; Allison, David B (2016) Inappropriate statistical method in a parallel-group randomized controlled trial results in unsubstantiated conclusions. Nutr J 15:58|
|Ivanescu, A E; Li, P; George, B et al. (2016) The importance of prediction model validation and assessment in obesity and nutrition research. Int J Obes (Lond) 40:887-94|
|Lewis Jr, D W; Fields, D A; Allison, D B (2016) Inconsistencies and inaccuracies in reporting on choice of endpoints and of statistical results in RCT of maternal diet. Pediatr Obes 11:e16-e17|
|George, Brandon J; Goldsby, TaShauna U; Brown, Andrew W et al. (2016) Unsubstantiated conclusions from improper statistical design and analysis of a randomized controlled trial. Int J Yoga 9:87-8|
|George, Brandon J; Brown, Andrew W; Allison, David B (2015) Errors in statistical analysis and questionable randomization lead to unreliable conclusions. J Paramed Sci 6:153-154|
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