• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种用于多变量纵向数据和参数加速失效时间联合分析的贝叶斯方法。

A Bayesian approach to joint analysis of multivariate longitudinal data and parametric accelerated failure time.

作者信息

Luo Sheng

机构信息

Division of Biostatistics, University of Texas School of Public Health, 1200 Pressler St., Houston, TX 77030, U.S.A.

出版信息

Stat Med. 2014 Feb 20;33(4):580-94. doi: 10.1002/sim.5956. Epub 2013 Sep 6.

DOI:10.1002/sim.5956
PMID:24009073
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3947121/
Abstract

Impairment caused by Parkinson's disease (PD) is multidimensional (e.g., sensoria, functions, and cognition) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of PD use multiple categorical and continuous longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we consider a joint random-effects model for the correlated outcomes. A multilevel item response theory model is used for the multivariate longitudinal outcomes and a parametric accelerated failure time model is used for the failure time because of the violation of proportional hazard assumption. These two models are linked via random effects. The Bayesian inference via MCMC is implemented in 'BUGS' language. Our proposed method is evaluated by a simulation study and is applied to DATATOP study, a motivating clinical trial to determine if deprenyl slows the progression of PD.

摘要

帕金森病（PD）所导致的损害具有多维度性（如感觉、功能和认知方面）且呈进行性发展。其多维度特性使得无法用单一结果来衡量疾病进展。PD的临床试验采用多种分类和连续的纵向结果来评估治疗对整体改善情况的效果。诸如死亡或退出试验等终末事件会中断随访过程。此外，发生终末事件的时间可能取决于多变量纵向测量结果。在本文中，我们考虑针对相关结果的联合随机效应模型。由于违反了比例风险假设，对于多变量纵向结果使用多级项目反应理论模型，对于失效时间则使用参数化加速失效时间模型。这两个模型通过随机效应相联系。通过马尔可夫链蒙特卡罗（MCMC）进行的贝叶斯推断是用“BUGS”语言实现的。我们所提出的方法通过模拟研究进行评估，并应用于DATATOP研究，这是一项旨在确定丙炔苯丙胺是否能减缓PD进展的具有启发性的临床试验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b98d/3947121/1b3fcffa0c67/nihms528526f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b98d/3947121/43003fa04f5f/nihms528526f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b98d/3947121/d52800b7a5b9/nihms528526f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b98d/3947121/6ca99db5b41d/nihms528526f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b98d/3947121/1b3fcffa0c67/nihms528526f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b98d/3947121/43003fa04f5f/nihms528526f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b98d/3947121/d52800b7a5b9/nihms528526f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b98d/3947121/6ca99db5b41d/nihms528526f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b98d/3947121/1b3fcffa0c67/nihms528526f4.jpg

相似文献

1
A Bayesian approach to joint analysis of multivariate longitudinal data and parametric accelerated failure time.一种用于多变量纵向数据和参数加速失效时间联合分析的贝叶斯方法。
Stat Med. 2014 Feb 20;33(4):580-94. doi: 10.1002/sim.5956. Epub 2013 Sep 6.
2
Joint modeling of multivariate longitudinal measurements and survival data with applications to Parkinson's disease.多变量纵向测量与生存数据的联合建模及其在帕金森病中的应用
Stat Methods Med Res. 2016 Aug;25(4):1346-58. doi: 10.1177/0962280213480877. Epub 2013 Apr 16.
3
Robust Bayesian inference for multivariate longitudinal data by using normal/independent distributions.使用正态/独立分布对多元纵向数据进行稳健贝叶斯推断。
Stat Med. 2013 Sep 30;32(22):3812-28. doi: 10.1002/sim.5778. Epub 2013 Mar 11.
4
Bayesian hierarchical model for multiple repeated measures and survival data: an application to Parkinson's disease.用于多重重复测量和生存数据的贝叶斯层次模型：在帕金森病中的应用
Stat Med. 2014 Oct 30;33(24):4279-91. doi: 10.1002/sim.6228. Epub 2014 Jun 17.
5
Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model.多维潜在特质线性混合模型在多次重复测量和生存数据中的联合建模。
Stat Methods Med Res. 2019 Oct-Nov;28(10-11):3392-3403. doi: 10.1177/0962280218802300. Epub 2018 Oct 11.
6
Robust Bayesian hierarchical model using normal/independent distributions.使用正态/独立分布的稳健贝叶斯层次模型。
Biom J. 2016 Jul;58(4):831-51. doi: 10.1002/bimj.201400255. Epub 2015 Dec 29.
7
Bayesian multivariate augmented Beta rectangular regression models for patient-reported outcomes and survival data.用于患者报告结局和生存数据的贝叶斯多元增强贝塔矩形回归模型。
Stat Methods Med Res. 2017 Aug;26(4):1684-1699. doi: 10.1177/0962280215586010. Epub 2015 Jun 2.
8
Multidimensional latent trait linear mixed model: an application in clinical studies with multivariate longitudinal outcomes.多维潜在特质线性混合模型：在具有多变量纵向结果的临床研究中的应用。
Stat Med. 2017 Sep 10;36(20):3244-3256. doi: 10.1002/sim.7347. Epub 2017 Jun 1.
9
Bayesian multiple imputation for missing multivariate longitudinal data from a Parkinson's disease clinical trial.帕金森病临床试验中缺失多变量纵向数据的贝叶斯多重填补法
Stat Methods Med Res. 2016 Apr;25(2):821-37. doi: 10.1177/0962280212469358. Epub 2012 Dec 12.
10
A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects.一种用于纵向测量和具有异质性随机效应的竞争风险生存数据的通用联合模型。
Lifetime Data Anal. 2011 Jan;17(1):80-100. doi: 10.1007/s10985-010-9169-6. Epub 2010 Jun 12.

引用本文的文献

1
Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective.基于条件分布的纵向响应与事件发生时间数据的联合建模：贝叶斯视角
J Appl Stat. 2021 Mar 9;49(9):2228-2245. doi: 10.1080/02664763.2021.1897971. eCollection 2022.
2
A flexible joint model for multiple longitudinal biomarkers and a time-to-event outcome: With applications to dynamic prediction using highly correlated biomarkers.一种用于多个纵向生物标志物和事件时间结局的灵活联合模型：在使用高度相关生物标志物进行动态预测中的应用。
Biom J. 2021 Dec;63(8):1575-1586. doi: 10.1002/bimj.202000085. Epub 2021 Jul 17.
3

本文引用的文献

1
Joint modeling of multivariate longitudinal measurements and survival data with applications to Parkinson's disease.多变量纵向测量与生存数据的联合建模及其在帕金森病中的应用
Stat Methods Med Res. 2016 Aug;25(4):1346-58. doi: 10.1177/0962280213480877. Epub 2013 Apr 16.
2
integIRTy: a method to identify genes altered in cancer by accounting for multiple mechanisms of regulation using item response theory.integIRTy：一种使用项目反应理论来识别癌症中受多种调控机制影响的基因的方法。
Bioinformatics. 2012 Nov 15;28(22):2861-9. doi: 10.1093/bioinformatics/bts561. Epub 2012 Sep 26.
3
Joint latent class models for longitudinal and time-to-event data: a review.
Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.
将纵向生物标志物纳入大数据时代的动态风险预测中：一种伪观测方法。
Stat Med. 2020 Nov 20;39(26):3685-3699. doi: 10.1002/sim.8687. Epub 2020 Jul 27.
4
Estimating the Evolution of Disease in the Parkinson's Progression Markers Initiative.估计帕金森进展标志物倡议中的疾病演变。
Neurodegener Dis. 2018;18(4):173-190. doi: 10.1159/000488780. Epub 2018 Aug 8.
5
Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues.事件发生时间与多变量纵向结果的联合建模：最新进展与问题
BMC Med Res Methodol. 2016 Sep 7;16(1):117. doi: 10.1186/s12874-016-0212-5.
6
Bayesian joint ordinal and survival modeling for breast cancer risk assessment.用于乳腺癌风险评估的贝叶斯联合有序和生存建模
Stat Med. 2016 Dec 10;35(28):5267-5282. doi: 10.1002/sim.7065. Epub 2016 Aug 14.
7
Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective.纵向零膨胀计数和事件时间数据的联合建模：贝叶斯视角。
Stat Methods Med Res. 2018 Apr;27(4):1258-1270. doi: 10.1177/0962280216659312. Epub 2016 Jul 26.
8
Predicting Clinical Binary Outcome Using Multivariate Longitudinal Data: Application to Patients with Newly Diagnosed Primary Open-Angle Glaucoma.使用多变量纵向数据预测临床二元结局：在新诊断的原发性开角型青光眼患者中的应用
J Biom Biostat. 2015 Oct;6(4). doi: 10.4172/2155-6180.1000254. Epub 2015 Oct 26.
9
Bayesian analysis of multi-type recurrent events and dependent termination with nonparametric covariate functions.具有非参数协变量函数的多类型复发事件和相关终止的贝叶斯分析。
Stat Methods Med Res. 2017 Dec;26(6):2869-2884. doi: 10.1177/0962280215613378. Epub 2015 Nov 6.
10
Bayesian multivariate augmented Beta rectangular regression models for patient-reported outcomes and survival data.用于患者报告结局和生存数据的贝叶斯多元增强贝塔矩形回归模型。
Stat Methods Med Res. 2017 Aug;26(4):1684-1699. doi: 10.1177/0962280215586010. Epub 2015 Jun 2.
纵向和生存数据的联合潜在类别模型：综述。
Stat Methods Med Res. 2014 Feb;23(1):74-90. doi: 10.1177/0962280212445839. Epub 2012 Apr 19.
4
Pattern mixture models and latent class models for the analysis of multivariate longitudinal data with informative dropouts.用于分析具有信息性缺失的多变量纵向数据的模式混合模型和潜在类别模型。
Int J Biostat. 2008;4(1):Article 14. doi: 10.2202/1557-4679.1088.
5
A review of multivariate longitudinal data analysis.多元纵向数据分析综述。
Stat Methods Med Res. 2011 Aug;20(4):299-330. doi: 10.1177/0962280209340191. Epub 2010 Mar 8.
6
Using global statistical tests in long-term Parkinson's disease clinical trials.在帕金森病长期临床试验中使用全局统计检验。
Mov Disord. 2009 Sep 15;24(12):1732-9. doi: 10.1002/mds.22645.
7
A Bayesian semiparametric survival model with longitudinal markers.具有纵向标志物的贝叶斯半参数生存模型。
Biometrics. 2010 Jun;66(2):435-43. doi: 10.1111/j.1541-0420.2009.01276.x. Epub 2009 Jun 8.
8
Analysis of longitudinal randomized clinical trials using item response models.应用项目反应模型分析纵向随机临床试验。
Contemp Clin Trials. 2009 Mar;30(2):158-70. doi: 10.1016/j.cct.2008.12.003. Epub 2008 Dec 24.
9
A randomized, double-blind, placebo-controlled, delayed start study to assess rasagiline as a disease modifying therapy in Parkinson's disease (the ADAGIO study): rationale, design, and baseline characteristics.一项评估雷沙吉兰作为帕金森病疾病修饰治疗的随机、双盲、安慰剂对照、延迟启动研究（ADAGIO研究）：原理、设计和基线特征
Mov Disord. 2008 Nov 15;23(15):2194-201. doi: 10.1002/mds.22218.
10
A rank-based sample size method for multiple outcomes in clinical trials.一种用于临床试验中多结局的基于秩的样本量方法。
Stat Med. 2008 Jul 20;27(16):3084-104. doi: 10.1002/sim.3182.