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Review
. 2023 Dec;24(12):e13635.
doi: 10.1111/obr.13635. Epub 2023 Sep 4.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research

Roger S Zoh  1 , Bridget H Esteves  2 , Xiaoxin Yu  1 , Amanda J Fairchild  3 , Ana I Vazquez  4 , Andrew G Chapple  5 , Andrew W Brown  6 , Brandon George  7 , Derek Gordon  8 , Douglas Landsittel  1 , Gary L Gadbury  9 , Greg Pavela  10 , Gustavo de Los Campos  11 , Luis M Mestre  12 , David B Allison  1
Affiliations
Review

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research

Roger S Zoh et al. Obes Rev. 2023 Dec.

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.

Keywords: heterogeneity of treatment effect; personalized medicine; personalized nutrition; tailored treatment.

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Conflict of interest statement

Conflicts of Interest

BAH is now an employee of the Kraft Heinz Company, Chicago, IL, USA. However, this work was initiated while BAH was affiliated with the University of Illinois at Urbana-Champaign. DBA and his institution have received consulting fees, grants, contracts, and donations from multiple for-profit entities with interests in obesity, nutrition, statistics, and clinical trials, but none supported or are directly related to this manuscript. No other conflicts of interest are declared.

Figures

Figure 1
Figure 1. Examples of interaction effects, main effects, and simple effects for a 2×2 factorial design.
This example assumes two factors (A and B) each with two levels (1 and 2). Consider from the text the examples of factor A representing sucralose, with level 1 being no sucralose and level 2 being sucralose, and similarly for factor B representing sucrose, with level 1 being no sucrose and level 2 being sucrose. Interaction effects should be formally tested before attempting to test for or interpret main effects because by definition the effect of one factor depends on the effect of another. However, simple effects can be tested whether or not there is an interaction effect. If the researcher is willing to accept there is no interaction effect, then main effects are more highly powered. See Figure 2 for examples of interaction effects.
Figure 2.
Figure 2.
The Balaam design with treatment sequence CC, CT, TC, and TT. Individuals are randomized to each of the 4 treatment sequences.
See this image and copyright information in PMC

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