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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