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Derivation of maternal dietary patterns accounting for regional heterogeneity

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  • Briana J. K. Stephenson
  • Amy H. Herring
  • Andrew F. Olshan

Abstract

Latent class models are often used to characterise dietary patterns. Yet, when subtle variations exist across different sub‐populations, overall population patterns can be masked and affect statistical inference on health outcomes. We address this concern with a flexible supervised clustering approach, introduced as Supervised Robust Profile Clustering, that identifies outcome‐dependent population‐based patterns, while partitioning out subpopulation pattern differences. Using dietary data from the 1997–2011 National Birth Defects Prevention Study, we determine how maternal dietary profiles associate with orofacial clefts among offspring. Results indicate mothers who consume a higher proportion of fruits and vegetables compared to land meats lower the proportion of progeny with orofacial cleft defect.

Suggested Citation

  • Briana J. K. Stephenson & Amy H. Herring & Andrew F. Olshan, 2022. "Derivation of maternal dietary patterns accounting for regional heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1957-1977, November.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1957-1977
    DOI: 10.1111/rssc.12604
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    References listed on IDEAS

    as
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    4. Briana J. K. Stephenson & Amy H. Herring & Andrew Olshan, 2020. "Robust Clustering With Subpopulation-Specific Deviations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 521-537, April.
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