Statistical Approach in Personalized Nutrition Exemplified by Reanalysis of Public Datasets

Paola G. Ferrario (), Maik Döring and Christian Ritz
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Paola G. Ferrario: Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, 76131 Karlsruhe, Germany
Maik Döring: National Reference Centre for Authentic Food, Max Rubner-Institut, 95326 Kulmbach, Germany
Christian Ritz: National Institute of Public Health, University of Southern Denmark, 1455 Copenhagen K, Denmark

Data, 2025, vol. 10, issue 2, 1-12

Abstract: In clinical nutrition, it is regularly observed that individuals respond differently to a dietary treatment. Personalized nutrition aims to consider such variability in response by delivering personalized nutritional recommendations. Ideally, the optimal treatment for each individual will be selected and then dispensed according to the specific individual’s characteristics. The aim of this paper is to discuss and apply existing statistical methods, which can be adequately used in the context of personalized nutrition. We discuss the estimation of individualized treatment rules (ITRs) as we wish to favor one out of two interventions. The applicability of the methods is demonstrated by reusing two public datasets: one in the context of a parallel group design and one in the context of a crossover design. The bias of the estimator of the ITRs underlying parameters is evaluated in a simulation study.

Keywords: personalized nutrition; individualized treatment rules; simulations; real data example; publicly available datasets; parallel group and crossover design (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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