Checking Fine and Gray subdistribution hazards model with cumulative sums of residuals.
Checking Fine and Gray subdistribution hazards model with cumulative sums of residuals.
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DOI：
10.1007/s10985-014-9313-9
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发表时间：
2015-04
影响因子：
1.3
通讯作者：
Zhang, Mei-Jie
中科院分区：
文献类型：
--
作者：
Li, Jianing;Scheike, Thomas H.;Zhang, Mei-Jie
关键词：
最近，人们提出了一种次分布风险函数的半参数比例回归模型，该模型被广泛应用于竞争风险数据的分析。然而，模型充分性的失败可能会导致参数估计中的严重偏差，并且只有有限的贡献来检验模型假设。本文提出了一类检验Fine和Gray模型假设的分析方法和图解方法。拟合度检验方法基于累积残差和，从三个方面验证了模型的有效性：(1)风险比的比例；(2)线性函数形式；(3)连接函数。对于每个假设测试，我们使用基于模拟的方法提供p值和针对零假设的可视化曲线图。我们还考虑对任何模型错误说明进行全面评估的综合测试。所提出的检验在仿真研究中表现良好，并用两个真实数据实例进行了说明。
Recently, proposed a semi-parametric proportional regression model for the subdistribution hazard function which has been used extensively for analyzing competing risks data. However, failure of model adequacy could lead to severe bias in parameter estimation, and only a limited contribution has been made to check the model assumptions. In this paper, we present a class of analytical methods and graphical approaches for checking the assumptions of Fine and Gray’s model. The proposed goodness-of-fit test procedures are based on the cumulative sums of residuals, which validate the model in three aspects: (1) proportionality of hazard ratio, (2) the linear functional form and (3) the link function. For each assumption testing, we provide a p-values and a visualized plot against the null hypothesis using a simulation-based approach. We also consider an omnibus test for overall evaluation against any model misspecification. The proposed tests perform well in simulation studies and are illustrated with two real data examples.
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