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Conditional Gaussian mixture modelling for dietary pattern analysis

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  • Michael T. Fahey
  • Christopher W. Thane
  • Gemma D. Bramwell
  • W. Andy Coward

Abstract

Summary. Free‐living individuals have multifaceted diets and consume foods in numerous combinations. In epidemiological studies it is desirable to characterize individual diets not only in terms of the quantity of individual dietary components but also in terms of dietary patterns. We describe the conditional Gaussian mixture model for dietary pattern analysis and show how it can be adapted to take account of important characteristics of self‐reported dietary data. We illustrate this approach with an analysis of the 2000–2001 National Diet and Nutrition Survey of adults. The results strongly favoured a mixture model solution allowing clusters to vary in shape and size, over the standard approach that has been used previously to find dietary patterns.

Suggested Citation

  • Michael T. Fahey & Christopher W. Thane & Gemma D. Bramwell & W. Andy Coward, 2007. "Conditional Gaussian mixture modelling for dietary pattern analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 149-166, January.
    • Handle: RePEc:bla:jorssa:v:170:y:2007:i:1:p:149-166
    DOI: 10.1111/j.1467-985X.2006.00452.x
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    Cited by:

    1. Li, Rui & Reich, Brian J. & Bondell, Howard D., 2021. "Deep distribution regression," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Laura C. Dawkins & Daniel B. Williamson & Stewart W. Barr & Sally R. Lampkin, 2020. "‘What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 251-280, January.
    3. Nocella, Giuseppe & Srinivasan, C.S., 2019. "Adherence to WHO’s nutrition recommendations in the UK: Dietary patterns and policy implications from a national survey," Food Policy, Elsevier, vol. 86(C), pages 1-1.

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