Design,analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research |
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| Authors: | Roger S. Zoh Bridget H. Esteves Xiaoxin Yu Amanda J. Fairchild Ana I. Vazquez Andrew G. Chapple Andrew W. Brown Brandon George Derek Gordon Douglas Landsittel Gary L. Gadbury Greg Pavela Gustavo de los Campos Luis M. Mestre David B. Allison |
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| Affiliation: | 1. Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA;2. Glanbia Performance Nutrition, Downers Grove, Illinois, USA;3. Department of Psychology, University of South Carolina, Columbia, South Carolina, USA;4. Department of Epidemiology and Biostatistics, Michigan State University, Lansing, Michigan, USA;5. Biostatistics Program, School of Public Health, LSU Health Sciences Center, New Orleans, Louisiana, USA;6. Department of Applied Health Science, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA;7. College of Population Health, Thomas Jefferson University, Philadelphia, Pennsylvania, USA;8. Department of Genetics, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA;9. Department of Statistics, Kansas State University, Manhattan, Kansa, USA;10. Department of Health Behavior, University of Alabama at Birmingham, Birmingham, Alabama, USA;11. Departments of Epidemiology & Biostatistics and Statistics & Probability, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, Lansing, Michigan, USA;12. Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA |
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| Abstract: | It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine. |
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| Keywords: | heterogeneity of treatment effect personalized medicine personalized nutrition tailored treatment |
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