Department of Biostatistics, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-0061, Japan.
Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
BMC Med Res Methodol. 2019 Feb 14;19(1):31. doi: 10.1186/s12874-019-0677-0.
In some clinical situations, patients experience repeated events of the same type. Among these, cancer recurrences can result in terminal events such as death. Therefore, here we dynamically predicted the risks of repeated and terminal events given longitudinal histories observed before prediction time using dynamic pseudo-observations (DPOs) in a landmarking model.
The proposed DPOs were calculated using Aalen-Johansen estimator for the event processes described in the multi-state model. Furthermore, in the absence of a terminal event, a more convenient approach without matrix operation was described using the ordering of repeated events. Finally, generalized estimating equations were used to calculate probabilities of repeated and terminal events, which were treated as multinomial outcomes.
Simulation studies were conducted to assess bias and investigate the efficiency of the proposed DPOs in a finite sample. Little bias was detected in DPOs even under relatively heavy censoring, and the method was applied to data from patients with colorectal liver metastases.
The proposed method enabled intuitive interpretations of terminal event settings.
在某些临床情况下，患者会经历同一类型的重复事件。其中，癌症复发可能导致死亡等终末事件。因此，我们在这里使用 landmarking 模型中的动态伪观测值（DPO），针对预测时间之前观察到的纵向历史记录，对重复和终末事件的风险进行了动态预测。
使用多状态模型中描述的事件过程的 Aalen-Johansen 估计器计算了所提出的 DPO。此外，在没有终末事件的情况下，还描述了一种使用重复事件排序的、无需矩阵运算的更方便方法。最后，使用广义估计方程计算重复和终末事件的概率，这些概率被视为多项结果。
进行了模拟研究，以评估有限样本中 DPO 的偏差和效率。即使在相对较重的删失下，DPO 也几乎没有偏差，并且该方法应用于结直肠癌肝转移患者的数据。
所提出的方法使终末事件设置具有直观的解释。
