{"title":"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","authors":"Zihang Lu, Padmaja Subbarao, Wendy Lou","doi":"10.1093/jrsssc/qlad100","DOIUrl":null,"url":null,"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.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Bayesian latent class model for integrating multi-source longitudinal data: application to the CHILD cohort study\",\"authors\":\"Zihang Lu, Padmaja Subbarao, Wendy Lou\",\"doi\":\"10.1093/jrsssc/qlad100\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":49981,\"journal\":{\"name\":\"Journal of the Royal Statistical Society Series C-Applied Statistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Royal Statistical Society Series C-Applied Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/jrsssc/qlad100\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Royal Statistical Society Series C-Applied Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jrsssc/qlad100","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
A Bayesian latent class model for integrating multi-source longitudinal data: application to the CHILD cohort study
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.
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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