用于纵向生物标志物和复发事件的参数化潜在类别联合模型
Parametric latent class joint model for a longitudinal biomarker and recurrent events.
作者信息
Han Jun, Slate Elizabeth H, Peña Edsel A
机构信息
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA.
出版信息
Stat Med. 2007 Dec 20;26(29):5285-302. doi: 10.1002/sim.2915.
Abstract
A joint model for a longitudinal biomarker and recurrent events is proposed. This general model accommodates the effects of covariates on the biomarker and event processes, the effects of accumulating event occurrences, and effects caused by interventions after each event occurrence. Association between the biomarker and recurrent event processes is captured through a latent class structure, which also serves to handle an underlying heterogeneous population. We use the EM algorithm for maximum likelihood estimation of the model parameters and a penalized likelihood measure to determine the number of latent classes. This joint model is validated by simulation and illustrated with a data set from epileptic seizure study.
摘要
提出了一种用于纵向生物标志物和复发事件的联合模型。这个通用模型考虑了协变量对生物标志物和事件过程的影响、累积事件发生的影响以及每次事件发生后干预措施所产生的影响。生物标志物和复发事件过程之间的关联通过一个潜在类别结构来捕捉，该结构也用于处理潜在的异质人群。我们使用期望最大化（EM）算法对模型参数进行最大似然估计，并使用惩罚似然度量来确定潜在类别的数量。这个联合模型通过模拟进行了验证，并以癫痫发作研究的数据集为例进行了说明。
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本文引用的文献
1
Semiparametric Inference for a General Class of Models for Recurrent Events.复发事件一般模型类别的半参数推断
J Stat Plan Inference. 2007 Jun 1;137(6):1727-1747. doi: 10.1016/j.jspi.2006.05.004.
2
THE LIPIDS OF THE BLOOD PLASMA IN EPILEPSY: II. Variations of Lipids in Relation to Occurrence of Seizures.癫痫患者血浆中的脂质：II. 脂质变化与癫痫发作的关系
J Clin Invest. 1933 Mar;12(2):255-65. doi: 10.1172/JCI100500.
3
Modelling intervention effects after cancer relapses.癌症复发后干预效果的建模。
Stat Med. 2005 Dec 30;24(24):3959-75. doi: 10.1002/sim.2394.
4
Latent pattern mixture models for informative intermittent missing data in longitudinal studies.纵向研究中用于信息性间歇性缺失数据的潜在模式混合模型。
Biometrics. 2004 Jun;60(2):295-305. doi: 10.1111/j.0006-341X.2004.00173.x.
5
Joint modelling of longitudinal measurements and event time data.纵向测量数据与事件时间数据的联合建模
Biostatistics. 2000 Dec;1(4):465-80. doi: 10.1093/biostatistics/1.4.465.
6
Biomarkers and surrogate endpoints: preferred definitions and conceptual framework.生物标志物与替代终点：首选定义及概念框架
Clin Pharmacol Ther. 2001 Mar;69(3):89-95. doi: 10.1067/mcp.2001.113989.
7
Estimating degradation by a Wiener diffusion process subject to measurement error.通过受测量误差影响的维纳扩散过程估计降解。
Lifetime Data Anal. 1995;1(3):307-19. doi: 10.1007/BF00985762.
8
A joint model for survival and longitudinal data measured with error.一种用于具有测量误差的生存数据和纵向数据的联合模型。
Biometrics. 1997 Mar;53(1):330-9.
9
Pattern-mixture models for multivariate incomplete data with covariates.具有协变量的多元不完全数据的模式混合模型。
Biometrics. 1996 Mar;52(1):98-111.
10
Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach.同时对删失生存数据和重复测量的协变量进行建模：一种吉布斯抽样方法。
Stat Med. 1996 Aug 15;15(15):1663-85. doi: 10.1002/(SICI)1097-0258(19960815)15:15<1663::AID-SIM294>3.0.CO;2-1.
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