Grand Mia K, de Witte Theo J M, Putter Hein
Department of Medical Statistics and Bioinformatics, 2300 RC, Leiden, The Netherlands.
Radboud University Medical Center, Radboud Institute of Molecular Life Sciences, Nijmegen, The Netherlands.
Biom J. 2018 Jul;60(4):734-747. doi: 10.1002/bimj.201700194. Epub 2018 Mar 25.
In recent years there have been a series of advances in the field of dynamic prediction. Among those is the development of methods for dynamic prediction of the cumulative incidence function in a competing risk setting. These models enable the predictions to be updated as time progresses and more information becomes available, for example when a patient comes back for a follow-up visit after completing a year of treatment, the risk of death, and adverse events may have changed since treatment initiation. One approach to model the cumulative incidence function in competing risks is by direct binomial regression, where right censoring of the event times is handled by inverse probability of censoring weights. We extend the approach by combining it with landmarking to enable dynamic prediction of the cumulative incidence function. The proposed models are very flexible, as they allow the covariates to have complex time-varying effects, and we illustrate how to investigate possible time-varying structures using Wald tests. The models are fitted using generalized estimating equations. The method is applied to bone marrow transplant data and the performance is investigated in a simulation study.
近年来，动态预测领域取得了一系列进展。其中包括在竞争风险环境下动态预测累积发病率函数的方法的发展。这些模型能够随着时间的推移和更多信息的获取而更新预测，例如，当患者在完成一年的治疗后回来进行随访时，自治疗开始以来死亡风险和不良事件可能已经发生了变化。在竞争风险中对累积发病率函数进行建模的一种方法是直接二项回归，其中事件时间的右删失通过删失权重的逆概率来处理。我们通过将其与地标法相结合来扩展该方法，以实现累积发病率函数的动态预测。所提出的模型非常灵活，因为它们允许协变量具有复杂的时变效应，并且我们说明了如何使用 Wald 检验来研究可能的时变结构。这些模型使用广义估计方程进行拟合。该方法应用于骨髓移植数据，并在模拟研究中对其性能进行了研究。
