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Statistical Approach in Personalized Nutrition Exemplified by Reanalysis of Public Datasets

Author

Listed:
  • 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)

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.

Suggested Citation

  • Paola G. Ferrario & Maik Döring & Christian Ritz, 2025. "Statistical Approach in Personalized Nutrition Exemplified by Reanalysis of Public Datasets," Data, MDPI, vol. 10(2), pages 1-12, January.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:2:p:18-:d:1580190
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    References listed on IDEAS

    as
    1. Roland A. Matsouaka & Junlong Li & Tianxi Cai, 2014. "Evaluating marker-guided treatment selection strategies," Biometrics, The International Biometric Society, vol. 70(3), pages 489-499, September.
    2. Alberto Caron & Gianluca Baio & Ioanna Manolopoulou, 2022. "Estimating individual treatment effects using non‐parametric regression models: A review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1115-1149, July.
    3. Tianxi Cai & T. Tony Cai & Zijian Guo, 2021. "Optimal statistical inference for individualized treatment effects in high‐dimensional models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 669-719, September.
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