利用纵向标志物对疾病进展进行建模。
Modeling Disease Progression with Longitudinal Markers.
作者信息
Inoue Lurdes Y T, Etzioni Ruth, Morrell Christopher, Müller Peter
机构信息
Department of Biostatistics, University of Washington, F-600 Health Sciences Building, Box 357232, Seattle, WA, 98195.
Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP 665, Box 19024, Seattle, WA, 98109.
出版信息
J Am Stat Assoc. 2008;103(481):259-270. doi: 10.1198/016214507000000356.
Abstract
In this paper we propose a Bayesian natural history model for disease progression based on the joint modeling of longitudinal biomarker levels, age at clinical detection of disease and disease status at diagnosis. We establish a link between the longitudinal responses and the natural history of the disease by using an underlying latent disease process which describes the onset of the disease and models the transition to an advanced stage of the disease as dependent on the biomarker levels. We apply our model to the data from the Baltimore Longitudinal Study of Aging on prostate specific antigen (PSA) to investigate the natural history of prostate cancer.
摘要
在本文中，我们基于纵向生物标志物水平、疾病临床检测时的年龄以及诊断时的疾病状态的联合建模，提出了一种用于疾病进展的贝叶斯自然史模型。我们通过使用一个潜在的潜伏疾病过程来建立纵向反应与疾病自然史之间的联系，该过程描述了疾病的发作，并将向疾病晚期的转变建模为依赖于生物标志物水平。我们将我们的模型应用于巴尔的摩衰老纵向研究中关于前列腺特异性抗原（PSA）的数据，以研究前列腺癌的自然史。
相似文献
1
Modeling Disease Progression with Longitudinal Markers.利用纵向标志物对疾病进展进行建模。
J Am Stat Assoc. 2008;103(481):259-270. doi: 10.1198/016214507000000356.
2
Predicting time to prostate cancer recurrence based on joint models for non-linear longitudinal biomarkers and event time outcomes.基于非线性纵向生物标志物和事件时间结果的联合模型预测前列腺癌复发时间
Stat Med. 2002 Dec 30;21(24):3897-911. doi: 10.1002/sim.1392.
3
Joint analysis of left-censored longitudinal biomarker and binary outcome via latent class modeling.基于潜在类别建模的左截断纵向生物标志物和二分类结局的联合分析。
Stat Med. 2018 Jun 15;37(13):2162-2173. doi: 10.1002/sim.7642. Epub 2018 Apr 2.
4
Joint Hidden Markov Model for Longitudinal and Time-to-Event Data with Latent Variables.具有潜在变量的纵向和时依事件数据的联合隐马尔可夫模型。
Multivariate Behav Res. 2022 Mar-May;57(2-3):441-457. doi: 10.1080/00273171.2020.1865864. Epub 2021 Jan 7.
5
Natural history of changes in prostate specific antigen in early stage prostate cancer.
J Urol. 1994 Nov;152(5 Pt 2):1743-8. doi: 10.1016/s0022-5347(17)32375-3.
6
A two-stage model in a Bayesian framework to estimate a survival endpoint in the presence of confounding by indication.在存在混杂因素的情况下，使用贝叶斯框架中的两阶段模型来估计生存终点。
Stat Methods Med Res. 2018 Apr;27(4):1271-1281. doi: 10.1177/0962280216660127. Epub 2016 Sep 1.
7
Bayesian latent time joint mixed effect models for multicohort longitudinal data.贝叶斯潜在时间联合混合效应模型在多队列纵向数据中的应用。
Stat Methods Med Res. 2019 Mar;28(3):835-845. doi: 10.1177/0962280217737566. Epub 2017 Nov 23.
8
Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data.纵向零膨胀计数和事件时间数据的联合线性和部分线性建模的近似贝叶斯推断。
Stat Methods Med Res. 2021 Jun;30(6):1484-1501. doi: 10.1177/09622802211002868. Epub 2021 Apr 19.
9
Comparison of Joint and Landmark Modeling for Predicting Cancer Progression in Men With Castration-Resistant Prostate Cancer: A Secondary Post Hoc Analysis of the PREVAIL Randomized Clinical Trial.比较用于预测去势抵抗性前列腺癌男性癌症进展的关节和标志建模：PREVAIL 随机临床试验的二次事后分析。
JAMA Netw Open. 2021 Jun 1;4(6):e2112426. doi: 10.1001/jamanetworkopen.2021.12426.
10
Bayesian piecewise mixture model for racial disparity in prostate cancer progression.用于前列腺癌进展中种族差异的贝叶斯分段混合模型。
Comput Stat Data Anal. 2012 Feb 1;56(2):362-369. doi: 10.1016/j.csda.2011.07.011.
引用本文的文献
1
Sequential Bayesian Registration for Functional Data.功能数据的序贯贝叶斯配准
Stat Comput. 2025;35(4):108. doi: 10.1007/s11222-025-10640-8. Epub 2025 May 27.
2
Economic evaluation of prostate cancer risk assessment methods: A cost-effectiveness analysis using population data.前列腺癌风险评估方法的经济学评价：基于人群数据的成本效果分析。
Cancer Med. 2023 Oct;12(19):20106-20118. doi: 10.1002/cam4.6587. Epub 2023 Sep 23.
3
Repeated measures discriminant analysis using multivariate generalized estimation equations.重复测量判别分析使用多变量广义估计方程。
Stat Methods Med Res. 2022 Apr;31(4):646-657. doi: 10.1177/09622802211032705. Epub 2021 Dec 13.
4
A Bivariate Mixed-Effects Location-Scale Model with application to Ecological Momentary Assessment (EMA) data.一种应用于生态瞬时评估（EMA）数据的双变量混合效应位置尺度模型。
Health Serv Outcomes Res Methodol. 2014 Dec;14(4):194-212. doi: 10.1007/s10742-014-0126-9.
5
Screening for prostate cancer in the US? Reduce the harms and keep the benefit.美国的前列腺癌筛查？减少危害并保留益处。
Int J Cancer. 2015 Apr 1;136(7):1600-7. doi: 10.1002/ijc.29136. Epub 2014 Sep 1.
6
Is prostate cancer screening cost-effective? A microsimulation model of prostate-specific antigen-based screening for British Columbia, Canada.前列腺癌筛查是否具有成本效益？加拿大不列颠哥伦比亚省基于前列腺特异性抗原筛查的微观模拟模型。
Int J Cancer. 2014 Aug 15;135(4):939-47. doi: 10.1002/ijc.28732. Epub 2014 Feb 4.
7
Evaluation of new technologies for cancer control based on population trends in disease incidence and mortality.基于疾病发病率和死亡率的人群趋势对癌症控制新技术的评估。
J Natl Cancer Inst Monogr. 2013;2013(46):117-23. doi: 10.1093/jncimonographs/lgt010.
8
Deriving benefit of early detection from biomarker-based prognostic models.从基于生物标志物的预后模型中获益。
Biostatistics. 2013 Jan;14(1):15-27. doi: 10.1093/biostatistics/kxs018. Epub 2012 Jun 22.
9
Screening for prostate cancer using multivariate mixed-effects models.使用多变量混合效应模型筛查前列腺癌。
J Appl Stat. 2012 Jun 1;39(6):1151-1175. doi: 10.1080/02664763.2011.644523.
10
What if I don't treat my PSA-detected prostate cancer? Answers from three natural history models.如果我不治疗前列腺特异性抗原（PSA）检测出的前列腺癌会怎样？来自三种自然病史模型的答案。
Cancer Epidemiol Biomarkers Prev. 2011 May;20(5):740-50. doi: 10.1158/1055-9965.EPI-10-0718.
本文引用的文献
1
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.随机松弛，吉布斯分布，以及贝叶斯图像恢复。
IEEE Trans Pattern Anal Mach Intell. 1984 Jun;6(6):721-41. doi: 10.1109/tpami.1984.4767596.
2
A natural history model of stage progression applied to breast cancer.应用于乳腺癌的阶段进展自然史模型。
Stat Med. 2007 Feb 10;26(3):581-95. doi: 10.1002/sim.2550.
3
Combining longitudinal studies of PSA.结合前列腺特异性抗原的纵向研究。
Biostatistics. 2004 Jul;5(3):483-500. doi: 10.1093/biostatistics/5.3.483.
4
The joint modeling of a longitudinal disease progression marker and the failure time process in the presence of cure.在存在治愈情况时纵向疾病进展标志物与失效时间过程的联合建模。
Biostatistics. 2002 Dec;3(4):547-63. doi: 10.1093/biostatistics/3.4.547.
5
Multistage carcinogenesis and the incidence of colorectal cancer.多阶段致癌作用与结直肠癌的发病率
Proc Natl Acad Sci U S A. 2002 Nov 12;99(23):15095-100. doi: 10.1073/pnas.222118199. Epub 2002 Nov 1.
6
Overdiagnosis due to prostate-specific antigen screening: lessons from U.S. prostate cancer incidence trends.前列腺特异性抗原筛查导致的过度诊断：来自美国前列腺癌发病率趋势的经验教训。
J Natl Cancer Inst. 2002 Jul 3;94(13):981-90. doi: 10.1093/jnci/94.13.981.
7
Modeling and optimization in early detection programs with a single exam.单次检查早期检测程序中的建模与优化
Biometrics. 2002 Mar;58(1):30-6. doi: 10.1111/j.0006-341x.2002.00030.x.
8
Hidden Markov models for the onset and progression of bronchiolitis obliterans syndrome in lung transplant recipients.肺移植受者闭塞性细支气管炎综合征发病和进展的隐马尔可夫模型
Stat Med. 2002 Jan 15;21(1):113-28. doi: 10.1002/sim.886.
9
Estimation and prediction for cancer screening models using deconvolution and smoothing.使用反卷积和平滑技术的癌症筛查模型的估计与预测
Biometrics. 2001 Jun;57(2):389-95. doi: 10.1111/j.0006-341x.2001.00389.x.
10
Modeling cancer detection: tumor size as a source of information on unobservable stages of carcinogenesis.癌症检测建模：肿瘤大小作为致癌作用不可观察阶段的信息来源。
Math Biosci. 2001 Jun;171(2):113-42. doi: 10.1016/s0025-5564(01)00058-x.
Zotero 插件