joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes.
joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes.
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DOI：
10.1186/s12874-018-0502-1
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发表时间：
2018-06-07
影响因子：
4
通讯作者：
Kolamunnage-Dona R
中科院分区：
文献类型：
--
作者：
Hickey GL;Philipson P;Jorgensen A;Kolamunnage-Dona R
近年来，纵向和事件发生时间结局的联合建模受到了相当大的关注。与此相适应的是，用于拟合这些模型的统计软件选项有所增加。然而，这些工具一般限于单一的纵向结果。在这里，我们描述了经典的联合模型的情况下，多个纵向结果，提出了一个实用的算法来拟合模型，并演示了如何拟合模型使用一个新的包的统计软件平台R，joineRML。多变量线性混合子模型被指定用于纵向结局，并且具有时变协变量的考克斯比例风险回归模型被指定用于事件时间子模型。模型之间的关联是通过一个零均值的多变量潜在高斯过程来捕获的。该模型拟合使用蒙特卡洛期望最大化算法，和推断的基础上近似的标准误差从经验的配置文件信息矩阵，这是一个替代的自举估计方法相比。我们用原发性胆汁性肝硬化患者的真实的数据示例说明了模型和软件，其中有三种重复测量的生物标志物。一个能够拟合多变量联合模型的开源软件包是可用的。底层算法和源代码使用了几种方法来提高计算速度。本文的在线版本（10.1186/s12874-018-0502-1）包含补充材料，可供授权用户使用。
Joint modelling of longitudinal and time-to-event outcomes has received considerable attention over recent years. Commensurate with this has been a rise in statistical software options for fitting these models. However, these tools have generally been limited to a single longitudinal outcome. Here, we describe the classical joint model to the case of multiple longitudinal outcomes, propose a practical algorithm for fitting the models, and demonstrate how to fit the models using a new package for the statistical software platform R, joineRML. A multivariate linear mixed sub-model is specified for the longitudinal outcomes, and a Cox proportional hazards regression model with time-varying covariates is specified for the event time sub-model. The association between models is captured through a zero-mean multivariate latent Gaussian process. The models are fitted using a Monte Carlo Expectation-Maximisation algorithm, and inferences are based on approximate standard errors from the empirical profile information matrix, which are contrasted to an alternative bootstrap estimation approach. We illustrate the model and software on a real data example for patients with primary biliary cirrhosis with three repeatedly measured biomarkers. An open-source software package capable of fitting multivariate joint models is available. The underlying algorithm and source code makes use of several methods to increase computational speed. The online version of this article (10.1186/s12874-018-0502-1) contains supplementary material, which is available to authorized users.
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影响因子：
2.3
作者：
Andrinopoulou, Eleni-Rosalina;Rizopoulos, D.;Lesaffre, E.
通讯作者：
Lesaffre, E.
影响因子：
6.2
作者：
Battes, Linda C.;Caliskan, Kadir;Kardys, Isabella
通讯作者：
Kardys, Isabella
影响因子：
2
作者：
Andrinopoulou, Eleni-Rosalina;Rizopoulos, Dimitris;Lesaffre, Emmanuel
通讯作者：
Lesaffre, Emmanuel
DOI：
10.1093/biostatistics/1.4.465
发表时间：
2000-12-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Henderson, R;Diggle, P;Dobson, A
通讯作者：
Dobson, A
影响因子：
45.3
作者：
Ibrahim, Joseph G.;Chu, Haitao;Chen, Liddy M.
通讯作者：
Chen, Liddy M.