A Bayesian latent class model for integrating multi-source longitudinal data: application to the CHILD cohort study

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-11-13 DOI:10.1093/jrsssc/qlad100

Zihang Lu, Padmaja Subbarao, Wendy Lou

引用次数: 1

Abstract

Abstract Multi-source longitudinal data have become increasingly common. This type of data refers to longitudinal datasets collected from multiple sources describing the same set of individuals. Representing distinct features of the individuals, each data source may consist of multiple longitudinal markers of distinct types and measurement frequencies. Motivated by the CHILD cohort study, we develop a model for joint clustering multi-source longitudinal data. The proposed model allows each data source to follow source-specific clustering, and they are aggregated to yield a global clustering. The proposed model is demonstrated through real-data analysis and simulation study.

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整合多源纵向数据的贝叶斯潜类模型:在儿童队列研究中的应用

摘要多源纵向数据越来越普遍。这种类型的数据是指从多个来源收集的描述同一组个体的纵向数据集。代表个体的不同特征，每个数据源可以由不同类型和测量频率的多个纵向标记组成。受CHILD队列研究的启发，我们开发了一个多源纵向数据联合聚类模型。所提出的模型允许每个数据源遵循特定于数据源的聚类，并将它们聚合以产生全局聚类。通过实际数据分析和仿真研究验证了该模型的有效性。

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

Journal of the Royal Statistical Society Series C-Applied Statistics 数学-统计学与概率论

CiteScore

2.50

自引率

0.00%

发文量

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.