A copula-based approach for dynamic prediction of survival with a binary time-dependent covariate.
A copula-based approach for dynamic prediction of survival with a binary time-dependent covariate.
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一种基于联结的方法，通过二元时间相关协变量动态预测生存。
DOI：
10.1002/sim.9102
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发表时间：
2021
影响因子：
2
通讯作者：
Tsodikov,Alexander
中科院分区：
文献类型：
--
作者：
Suresh,Krithika;Taylor,JeremyMG;Tsodikov,Alexander
动态预测方法结合纵向生物标志物信息，以产生更新的、更准确的条件生存概率预测。有两种获得动态预测的方法：（1）纵向标志物和生存过程的联合模型，以及（2）为联合分布的特定组分指定模型的近似方法。在二元标记的情况下，疾病-死亡模型是联合建模方法的一个例子，它是统一的，并产生一致的预测。然而，以前的文献表明，近似的方法，如地标，具有额外的灵活性，可以有良好的预测性能。一种这样的方法提出使用高斯copula来对条件连续标记和存活分布的联合分布进行建模。它的优点是为边缘指定已建立的灵活模型，可以评估拟合优度，并且可以在标准软件中实现简单的估计。在这篇文章中，我们提供了一个高斯copula方法的动态预测，以适应一个二进制标记使用连续的潜变量配方。我们比较了这种方法的预测性能，联合建模和地标模拟，并证明其用于获得动态预测的应用程序中的前列腺癌研究。
Dynamic prediction methods incorporate longitudinal biomarker information to produce updated, more accurate predictions of conditional survival probability. There are two approaches for obtaining dynamic predictions: (1) a joint model of the longitudinal marker and survival process, and (2) an approximate approach that specifies a model for a specific component of the joint distribution. In the case of a binary marker, an illness‐death model is an example of a joint modeling approach that is unified and produces consistent predictions. However, previous literature has shown that approximate approaches, such as landmarking, with additional flexibility can have good predictive performance. One such approach proposes using a Gaussian copula to model the joint distribution of conditional continuous marker and survival distributions. It has the advantage of specifying established, flexible models for the marginals for which goodness‐of‐fit can be assessed, and has easy estimation that can be implemented in standard software. In this article, we provide a Gaussian copula approach for dynamic prediction to accommodate a binary marker using a continuous latent variable formulation. We compare the predictive performance of this approach to joint modeling and landmarking using simulations and demonstrate its use for obtaining dynamic predictions in an application to a prostate cancer study.