解释区域异质性的母体饮食模式的推导

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2022-10-18 DOI:10.1111/rssc.12604
Briana J. K. Stephenson, Amy H. Herring, Andrew F. Olshan
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引用次数: 3

摘要

潜在类别模型通常用于描述饮食模式。然而,当不同亚群之间存在细微差异时,总体人口模式可能被掩盖,并影响对健康结果的统计推断。我们通过一种灵活的监督聚类方法来解决这个问题,该方法被称为监督鲁棒概要聚类,它可以识别结果依赖的基于种群的模式,同时划分出亚种群模式差异。利用1997-2011年国家出生缺陷预防研究的饮食数据,我们确定了母亲的饮食特征与后代的口面部裂之间的关系。结果表明,与陆地肉类相比,食用水果和蔬菜比例较高的母亲,其后代患口腔面部缺陷的比例较低。
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Derivation of maternal dietary patterns accounting for regional heterogeneity

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.

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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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