纵向和事件时间数据在R中的联合建模教程

Sezen Cekic, Stephen R. Aichele, A. Brandmaier, Ylva Kohncke, P. Ghisletta
{"title":"纵向和事件时间数据在R中的联合建模教程","authors":"Sezen Cekic, Stephen R. Aichele, A. Brandmaier, Ylva Kohncke, P. Ghisletta","doi":"10.5964/QCMB.2979","DOIUrl":null,"url":null,"abstract":"In biostatistics and medical research, longitudinal data are often composed of repeated assessments of a variable and dichotomous indicators to mark an event of interest. Consequently, joint modeling of longitudinal and time-to-event data has generated much interest in these disciplines over the previous decade. In behavioural sciences, too, often we are interested in relating individual trajectories and discrete events. Yet, joint modeling is rarely applied in behavioural sciences more generally. This tutorial presents an overview and general framework for joint modeling of longitudinal and time-to-event data, and fully illustrates its application in the context of a behavioral study with the JMbayes R package. In particular, the tutorial discusses practical topics, such as model selection and comparison, choice of joint modeling parameterization and interpretation of model parameters. In the end, this tutorial aims at introducing didactically the theory related to joint modeling and to introduce novice analysts to the use of the JMbayes package.","PeriodicalId":314301,"journal":{"name":"Quantitative and Computational Methods in Behavioral Sciences","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A tutorial for joint modeling of longitudinal and time-to-event data in R\",\"authors\":\"Sezen Cekic, Stephen R. Aichele, A. Brandmaier, Ylva Kohncke, P. Ghisletta\",\"doi\":\"10.5964/QCMB.2979\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In biostatistics and medical research, longitudinal data are often composed of repeated assessments of a variable and dichotomous indicators to mark an event of interest. Consequently, joint modeling of longitudinal and time-to-event data has generated much interest in these disciplines over the previous decade. In behavioural sciences, too, often we are interested in relating individual trajectories and discrete events. Yet, joint modeling is rarely applied in behavioural sciences more generally. This tutorial presents an overview and general framework for joint modeling of longitudinal and time-to-event data, and fully illustrates its application in the context of a behavioral study with the JMbayes R package. In particular, the tutorial discusses practical topics, such as model selection and comparison, choice of joint modeling parameterization and interpretation of model parameters. In the end, this tutorial aims at introducing didactically the theory related to joint modeling and to introduce novice analysts to the use of the JMbayes package.\",\"PeriodicalId\":314301,\"journal\":{\"name\":\"Quantitative and Computational Methods in Behavioral Sciences\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quantitative and Computational Methods in Behavioral Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5964/QCMB.2979\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative and Computational Methods in Behavioral Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5964/QCMB.2979","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

在生物统计学和医学研究中,纵向数据通常由对变量的重复评估和标记感兴趣事件的二分指标组成。因此,在过去十年中,纵向和事件时间数据的联合建模引起了这些学科的极大兴趣。在行为科学中,我们也常常对将个体轨迹和离散事件联系起来感兴趣。然而,联合建模很少在行为科学中得到更广泛的应用。本教程介绍了纵向和事件时间数据联合建模的概述和一般框架,并充分说明了它在使用JMbayes R包进行行为研究的上下文中的应用。特别地,本教程讨论了一些实用的主题,如模型的选择和比较、联合建模参数化的选择和模型参数的解释。最后,本教程旨在从教学上介绍与联合建模相关的理论,并向分析新手介绍JMbayes包的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A tutorial for joint modeling of longitudinal and time-to-event data in R
In biostatistics and medical research, longitudinal data are often composed of repeated assessments of a variable and dichotomous indicators to mark an event of interest. Consequently, joint modeling of longitudinal and time-to-event data has generated much interest in these disciplines over the previous decade. In behavioural sciences, too, often we are interested in relating individual trajectories and discrete events. Yet, joint modeling is rarely applied in behavioural sciences more generally. This tutorial presents an overview and general framework for joint modeling of longitudinal and time-to-event data, and fully illustrates its application in the context of a behavioral study with the JMbayes R package. In particular, the tutorial discusses practical topics, such as model selection and comparison, choice of joint modeling parameterization and interpretation of model parameters. In the end, this tutorial aims at introducing didactically the theory related to joint modeling and to introduce novice analysts to the use of the JMbayes package.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信