Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Australia.
Stat Methods Med Res. 2019 Dec;28(12):3502-3515. doi: 10.1177/0962280218808821. Epub 2018 Oct 31.
Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level in the hierarchy than the patient-level, since this has implications for the model formulation.
最近，人们对纵向和生存时间数据的联合建模给予了很多关注。越来越多的标准联合建模方法的扩展被提出，以处理应用研究中常见的复杂数据结构。在本文中，我们提出了一种用于层次化纵向和生存时间数据的联合模型。我们的动机应用探索了非小细胞肺癌患者肿瘤负担与无进展生存期之间的关联。我们将肿瘤负担定义为目标病变大小的函数，这些病变在患者内聚类。由于一个患者可能有多个病变，并且每个病变都随时间进行跟踪，因此数据具有三级层次结构：在时间点（第 1 级）上采集的重复测量值聚类在病变内（第 2 级），然后在患者内（第 3 级）聚类。我们通过指定新的关联结构来联合建模病变特异性纵向轨迹和患者特异性死亡或疾病进展风险，该关联结构将较低级别的聚类（例如病变）的信息组合到患者水平的汇总信息中（例如肿瘤负担）。我们提供了用户友好的软件，用于在贝叶斯框架下拟合模型。最后，我们讨论了在比患者级别更高的层次上出现额外聚类因素的情况，因为这对模型公式化有影响。
