The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC
The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC
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DOI：
10.18637/jss.v072.i07
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发表时间：
2016-08-01
影响因子：
5.8
通讯作者：
Rizopoulos, Dimitris
中科院分区：
文献类型：
--
作者：
Rizopoulos, Dimitris
纵向和时间-事件数据的联合模型构成了一个有吸引力的建模框架，近年来受到了很大的关注。本文介绍了能力的R包JMbayes拟合这些模型下的贝叶斯方法，使用马尔可夫链蒙特卡罗算法。JMbayes可以拟合广泛的联合模型，包括连续和分类纵向响应的联合模型，并提供了几个选项来建模两个结果之间的关联结构。此外，该软件包可用于推导这两种结果的动态预测，并提供了几种工具来验证这些预测的歧视和校准。所有这些功能说明使用一个真实的数据例原发性胆汁性肝硬化患者。
Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years. This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markov chain Monte Carlo algorithms. JMbayes can fit a wide range of joint models, including among others joint models for continuous and categorical longitudinal responses, and provides several options for modeling the association structure between the two outcomes. In addition, this package can be used to derive dynamic predictions for both outcomes, and offers several tools to validate these predictions in terms of discrimination and calibration. All these features are illustrated using a real data example on patients with primary biliary cirrhosis.