Modelling ethnic differences in the distribution of insulin resistance via Bayesian nonparametric processes: an application to the SABRE cohort study.

IF 1.2 4区数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Biostatistics Pub Date : 2020-08-31 DOI:10.1515/ijb-2019-0108

Marco Molinari, Maria de Iorio, Nishi Chaturvedi, Alun Hughes, Therese Tillin

引用次数: 0

Abstract

We analyse data from the Southall And Brent REvisited (SABRE) tri-ethnic study, where measurements of metabolic and anthropometric variables have been recorded. In particular, we focus on modelling the distribution of insulin resistance which is strongly associated with the development of type 2 diabetes. We propose the use of a Bayesian nonparametric prior to model the distribution of Homeostasis Model Assessment insulin resistance, as it allows for data-driven clustering of the observations. Anthropometric variables and metabolites concentrations are included as covariates in a regression framework. This strategy highlights the presence of sub-populations in the data, characterised by different levels of risk of developing type 2 diabetes across ethnicities. Posterior inference is performed through Markov Chains Monte Carlo (MCMC) methods.

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通过贝叶斯非参数过程模拟胰岛素抵抗分布的种族差异：在SABRE队列研究中的应用。

我们分析了来自Southall And Brent REvisited （SABRE）三种族研究的数据，其中记录了代谢和人体测量变量的测量值。特别是，我们专注于模拟胰岛素抵抗的分布，这与2型糖尿病的发展密切相关。我们建议使用贝叶斯非参数先验模型来模拟稳态模型评估胰岛素抵抗的分布，因为它允许数据驱动的观察聚类。在回归框架中，人体测量变量和代谢物浓度作为协变量包括在内。这一策略强调了数据中亚人群的存在，其特征是不同种族患2型糖尿病的风险水平不同。后验推理是通过马尔可夫链蒙特卡罗（MCMC）方法进行的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY

CiteScore

2.10

自引率

8.30%

发文量

审稿时长

>12 weeks

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.