Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data
Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data
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DOI：
10.1177/0962280215588340
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发表时间：
2017-08-01
影响因子：
2.3
通讯作者：
Lesaffre, E.
中科院分区：
文献类型：
--
作者：
Andrinopoulou, Eleni-Rosalina;Rizopoulos, D.;Lesaffre, E.
如今，医学界对个性化医疗和针对个别患者的需求量身定制决策的兴趣日益浓厚。在此背景下，我们的研究进展来自荷兰伊拉斯谟医学中心心胸外科的一项研究，该研究由在主动脉位置接受人体组织瓣膜的患者组成，随后对他们进行超声心动图监测。我们的目的是利用现有患者的随访测量，为未来患者提供生存和免于再干预的动态更新预测。在本文中，我们建议联合建模多个纵向测量结合竞争风险生存结果，并导出动态更新的累积发生率函数。此外，通过考虑不同类型的关联结构（如随时间变化的轨迹斜率和随时间变化的累积效应），我们研究了纵向过程的不同特征是否会显著改变对感兴趣事件的预测。我们最后的贡献集中在优化所得预测的质量。特别是，我们建议使用贝叶斯模型平均来适当地组合所有考虑模型的预测，而不是在忽略模型不确定性的候选模型列表中选择一个最终模型。
Nowadays there is an increased medical interest in personalized medicine and tailoring decision making to the needs of individual patients. Within this context our developments are motivated from a Dutch study at the Cardio-Thoracic Surgery Department of the Erasmus Medical Center, consisting of patients who received a human tissue valve in aortic position and who were thereafter monitored echocardiographically. Our aim is to utilize the available follow-up measurements of the current patients to produce dynamically updated predictions of both survival and freedom from re-intervention for future patients. In this paper, we propose to jointly model multiple longitudinal measurements combined with competing risk survival outcomes and derive the dynamically updated cumulative incidence functions. Moreover, we investigate whether different features of the longitudinal processes would change significantly the prediction for the events of interest by considering different types of association structures, such as time-dependent trajectory slopes and time-dependent cumulative effects. Our final contribution focuses on optimizing the quality of the derived predictions. In particular, instead of choosing one final model over a list of candidate models which ignores model uncertainty, we propose to suitably combine predictions from all considered models using Bayesian model averaging.