具有竞争风险结果的标记分类指标的高斯 Copula 回归建模

Alejandro Román Vásquez, Gabriel Escarela, H. Reyes-Cervantes, Gabriel Núñez-Antonio
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引用次数: 0

摘要

有关竞合风险的决策通常基于预测特定病因结果的连续值标记。标记物的分类能力可以用随时间变化的接收者操作特征曲线和相应的曲线下面积(AUC)来概括。本文提出了一种基于高斯协方差的模型来表示连续值标记物、总生存时间和病因特异性结果的联合分布。然后,该模型被用于描述竞争风险背景下的时变 ROC 曲线。通过将线性分量与倾斜正态分布的标记边缘、参数比例危险模型的生存时间和对数模型的失败原因联系起来,纳入了协变量效应。使用最大似然法进行估计,并通过自举技术获得 AUC 的置信区间。采用信息标准策略来寻找一个合理的模型。考虑到不同的样本量和普查分布,在模拟研究中对所提模型的性能进行了评估。通过对一项前列腺癌临床试验的重新分析,对这些方法进行了说明。在存在竞争风险的情况下,联合回归策略为随时间变化的 ROC 曲线建立了一个简单而灵活的模型,从而加深了对协变量如何影响标记物分辨能力的理解。
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Gaussian Copula Regression Modeling for Marker Classification Metrics with Competing Risk Outcomes
Decisions regarding competing risks are usually based on a continuous-valued marker toward predicting a cause-specific outcome. The classification power of a marker can be summarized using the time-dependent receiver operating characteristic curve and the corresponding area under the curve (AUC). This paper proposes a Gaussian copula-based model to represent the joint distribution of the continuous-valued marker, the overall survival time, and the cause-specific outcome. Then, it is used to characterize the time-varying ROC curve in the context of competing risks. Covariate effects are incorporated by linking linear components to the skewed normal distribution for the margin of the marker, a parametric proportional hazards model for the survival time, and a logit model for the cause of failure. Estimation is carried out using maximum likelihood, and a bootstrap technique is implemented to obtain confidence intervals for the AUC. Information-criteria strategies are employed to find a parsimonious model. The performance of the proposed model is evaluated in simulation studies, considering different sample sizes and censoring distributions. The methods are illustrated with the reanalysis of a prostate cancer clinical trial. The joint regression strategy produces a straightforward and flexible model of the time-dependent ROC curve in the presence of competing risks, enhancing the understanding of how covariates may affect the discrimination of a marker.
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