使用纵向和生存数据联合模型确定生物标志物的个性化筛查间隔。
Personalized screening intervals for biomarkers using joint models for longitudinal and survival data.
作者信息
Rizopoulos Dimitris, Taylor Jeremy M G, Van Rosmalen Joost, Steyerberg Ewout W, Takkenberg Johanna J M
机构信息
Department of Biostatistics, Erasmus University Medical Center, 3000 CE Rotterdam, The Netherlands
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
出版信息
Biostatistics. 2016 Jan;17(1):149-64. doi: 10.1093/biostatistics/kxv031. Epub 2015 Aug 28.
Abstract
Screening and surveillance are routinely used in medicine for early detection of disease and close monitoring of progression. Motivated by a study of patients who received a human tissue valve in the aortic position, in this work we are interested in personalizing screening intervals for longitudinal biomarker measurements. Our aim in this paper is 2-fold: First, to appropriately select the model to use at the time point the patient was still event-free, and second, based on this model to select the optimal time point to plan the next measurement. To achieve these two goals, we combine information theory measures with optimal design concepts for the posterior predictive distribution of the survival process given the longitudinal history of the subject.
摘要
筛查和监测在医学中常用于疾病的早期检测和病情进展的密切监测。受一项关于在主动脉位置接受人体组织瓣膜的患者的研究启发，在这项工作中，我们感兴趣的是为纵向生物标志物测量设定个性化的筛查间隔。本文的目标有两个：第一，在患者仍无事件发生的时间点适当选择要使用的模型；第二，基于该模型选择计划下一次测量的最佳时间点。为了实现这两个目标，我们将信息论度量与最优设计概念相结合，用于根据受试者的纵向病史得出生存过程的后验预测分布。
相似文献
1
Personalized screening intervals for biomarkers using joint models for longitudinal and survival data.使用纵向和生存数据联合模型确定生物标志物的个性化筛查间隔。
Biostatistics. 2016 Jan;17(1):149-64. doi: 10.1093/biostatistics/kxv031. Epub 2015 Aug 28.
2
A visualization method measuring the performance of biomarkers for guiding treatment decisions.一种用于评估生物标志物在指导治疗决策方面性能的可视化方法。
Pharm Stat. 2016 Mar-Apr;15(2):152-64. doi: 10.1002/pst.1728. Epub 2015 Dec 3.
3
Avoiding "toxic knowledge": the importance of framing personalized risk information in clinical decision-making.避免“有害知识”：在临床决策中构建个性化风险信息的重要性。
Per Med. 2021 Mar;18(2):91-95. doi: 10.2217/pme-2020-0174. Epub 2021 Feb 22.
4
A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing.用于个性化监测测试时间的信息价值框架。
Med Decis Making. 2022 May;42(4):474-486. doi: 10.1177/0272989X211049213. Epub 2021 Nov 7.
5
CRcoder: An Interactive Web Application and SAS Macro to Support Personalized Clinical Decisions.CRcoder：一个支持个性化临床决策的交互式网络应用程序和 SAS 宏。
Perm J. 2020;24. doi: 10.7812/TPP/19.078. Epub 2019 Dec 18.
6
Impact of Biomarkers on Personalized Medicine.生物标志物对个性化医疗的影响。
Handb Exp Pharmacol. 2016;232:285-311. doi: 10.1007/164_2015_24.
7
Unintended consequences for patients of future personalized pharmacoprinting.未来个性化药物打印对患者的意外后果。
Int J Clin Pharm. 2018 Apr;40(2):321-324. doi: 10.1007/s11096-018-0596-x.
8
Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data.重度主动脉瓣狭窄患者预后的动态预测：纵向数据和事件发生时间数据联合模型的应用
BMC Cardiovasc Disord. 2015 May 7;15:28. doi: 10.1186/s12872-015-0035-z.
9
Personalized dynamic risk assessment in nephrology is a next step in prognostic research.个性化动态风险评估在肾脏病学中是预后研究的下一步。
Kidney Int. 2018 Jul;94(1):214-217. doi: 10.1016/j.kint.2018.04.007. Epub 2018 May 24.
10
Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data.使用两个纵向结果的联合模型和竞争风险数据进行联合动态预测。
Stat Methods Med Res. 2017 Aug;26(4):1787-1801. doi: 10.1177/0962280215588340. Epub 2015 Jun 9.
引用本文的文献
1
DYNAMIC RISK PREDICTION FOR CERVICAL PRECANCER SCREENING WITH CONTINUOUS AND BINARY LONGITUDINAL BIOMARKERS.利用连续和二元纵向生物标志物进行宫颈癌前筛查的动态风险预测
Ann Appl Stat. 2024 Mar;18(1):246-265. doi: 10.1214/23-aoas1788. Epub 2024 Jan 31.
2
Optimal risk-assessment scheduling for primary prevention of cardiovascular disease.心血管疾病一级预防的最佳风险评估安排
J R Stat Soc Ser A Stat Soc. 2024 Sep 17;188(3):920-934. doi: 10.1093/jrsssa/qnae086. eCollection 2025 Jul.
3
Risk-Profile Based Monitoring Intervals for Multivariate Longitudinal Biomarker Measurements and Competing Events With Applications in Stable Heart Failure.基于风险概况的多变量纵向生物标志物测量及竞争事件监测间隔在稳定型心力衰竭中的应用
Stat Med. 2025 May;44(10-12):e70088. doi: 10.1002/sim.70088.
4
Constructing time-invariant dynamic surveillance rules for optimal monitoring schedules.构建时不变动态监测规则以优化监测计划。
Biometrics. 2023 Dec;79(4):3895-3906. doi: 10.1111/biom.13911. Epub 2023 Jul 21.
5
A multivariate joint model to adjust for random measurement error while handling skewness and correlation in dietary data in an epidemiologic study of mortality.在一项关于死亡率的流行病学研究中，为了调整饮食数据中的偏度和相关性，同时处理随机测量误差，提出了一种多变量联合模型。
Ann Epidemiol. 2023 Jun;82:8-15. doi: 10.1016/j.annepidem.2023.03.007. Epub 2023 Mar 25.
6
Construction of an anthropometric discriminant model for identification of elite swimmers: an adaptive lasso approach.构建一个人体测量判别模型以识别优秀游泳运动员：自适应套索方法。
PeerJ. 2023 Jan 9;11:e14635. doi: 10.7717/peerj.14635. eCollection 2023.
7
A Bayesian multilevel time-varying framework for joint modeling of hospitalization and survival in patients on dialysis.贝叶斯多层时变框架用于联合建模透析患者的住院和生存情况。
Stat Med. 2022 Dec 20;41(29):5597-5611. doi: 10.1002/sim.9582. Epub 2022 Oct 1.
8
Left ventricular ejection fraction and cardiac biomarkers for dynamic prediction of cardiotoxicity in early breast cancer.左心室射血分数和心脏生物标志物用于早期乳腺癌心脏毒性的动态预测
Front Cardiovasc Med. 2022 Aug 16;9:933428. doi: 10.3389/fcvm.2022.933428. eCollection 2022.
9
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.
10
A personalized and dynamic risk estimation model: The new paradigm in Barrett's esophagus surveillance.个性化和动态风险评估模型：巴雷特食管监测的新模式。
PLoS One. 2022 Apr 27;17(4):e0267503. doi: 10.1371/journal.pone.0267503. eCollection 2022.
本文引用的文献
1
Joint modeling of two longitudinal outcomes and competing risk data.两个纵向结局与竞争风险数据的联合建模
Stat Med. 2014 Aug 15;33(18):3167-78. doi: 10.1002/sim.6158. Epub 2014 Mar 27.
2
Real-time individual predictions of prostate cancer recurrence using joint models.使用联合模型对前列腺癌复发进行实时个体预测。
Biometrics. 2013 Mar;69(1):206-13. doi: 10.1111/j.1541-0420.2012.01823.x. Epub 2013 Feb 4.
3
Choice of prognostic estimators in joint models by estimating differences of expected conditional Kullback-Leibler risks.通过估计期望条件库尔贝克-莱布勒风险的差异来选择联合模型中的预后估计量。
Biometrics. 2012 Jun;68(2):380-7. doi: 10.1111/j.1541-0420.2012.01753.x. Epub 2012 May 11.
4
A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event.贝叶斯半参数多维联合模型用于多个纵向结局和一个生存时间。
Stat Med. 2011 May 30;30(12):1366-80. doi: 10.1002/sim.4205. Epub 2011 Feb 21.
5
Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data.纵向数据和事件发生时间数据联合模型中的动态预测与前瞻性准确性
Biometrics. 2011 Sep;67(3):819-29. doi: 10.1111/j.1541-0420.2010.01546.x. Epub 2011 Feb 9.
6
Re-operations for aortic allograft root failure: experience from a 21-year single-center prospective follow-up study.同种异体主动脉根部重建失败的再次手术：21 年单中心前瞻性随访研究的经验。
Eur J Cardiothorac Surg. 2011 Jul;40(1):35-42. doi: 10.1016/j.ejcts.2010.11.025. Epub 2011 Jan 11.
7
Bayesian Variable Selection and Computation for Generalized Linear Models with Conjugate Priors.具有共轭先验的广义线性模型的贝叶斯变量选择与计算
Bayesian Anal. 2008 Jul 1;3(3):585-614. doi: 10.1214/08-BA323.
Zotero 插件