Njagi Edmund Njeru, Molenberghs Geert, Kenward Michael G, Verbeke Geert, Rizopoulos Dimitris
I-BioStat, Universiteit Hasselt, B-3590, Diepenbeek, Belgium.
Biom J. 2014 Nov;56(6):1001-15. doi: 10.1002/bimj.201300028. Epub 2014 Jun 20.
We consider a conceptual correspondence between the missing data setting, and joint modeling of longitudinal and time-to-event outcomes. Based on this, we formulate an extended shared random effects joint model. Based on this, we provide a characterization of missing at random, which is in line with that in the missing data setting. The ideas are illustrated using data from a study on liver cirrhosis, contrasting the new framework with conventional joint models.
我们考虑了缺失数据设置与纵向和事件发生时间结局的联合建模之间的概念对应关系。基于此，我们构建了一个扩展的共享随机效应联合模型。基于此，我们给出了随机缺失的一种特征描述，这与缺失数据设置中的情况一致。我们使用一项关于肝硬化研究的数据来说明这些想法，将新框架与传统联合模型进行对比。
