School of Economics and Finance, Xi'an Jiaotong University, Xi'an, China.
Department of Mathematical Sciences, Loughborough University, Loughborough, UK.
Stat Med. 2024 Nov 30;43(27):5100-5114. doi: 10.1002/sim.10221. Epub 2024 Sep 29.
As a favorable alternative to the censored quantile regression, censored expectile regression has been popular in survival analysis due to its flexibility in modeling the heterogeneous effect of covariates. The existing weighted expectile regression (WER) method assumes that the censoring variable and covariates are independent, and that the covariates effects has a global linear structure. However, these two assumptions are too restrictive to capture the complex and nonlinear pattern of the underlying covariates effects. In this article, we developed a novel weighted expectile regression neural networks (WERNN) method by incorporating the deep neural network structure into the censored expectile regression framework. To handle the random censoring, we employ the inverse probability of censoring weighting (IPCW) technique in the expectile loss function. The proposed WERNN method is flexible enough to fit nonlinear patterns and therefore achieves more accurate prediction performance than the existing WER method for right censored data. Our findings are supported by extensive Monte Carlo simulation studies and a real data application.
作为一种替代有偏分位数回归的方法，有偏期望分位数回归由于其在建模协变量异质性效应方面的灵活性，在生存分析中得到了广泛应用。现有的加权期望分位数回归（WER）方法假设删失变量和协变量是独立的，并且协变量的效应具有全局线性结构。然而，这两个假设过于严格，无法捕捉到潜在协变量效应的复杂和非线性模式。在本文中，我们通过将深度神经网络结构纳入有偏期望分位数回归框架，开发了一种新的加权期望分位数回归神经网络（WERNN）方法。为了处理随机删失，我们在期望分位数损失函数中使用了逆概率删失加权（IPCW）技术。所提出的 WERNN 方法足够灵活，可以拟合非线性模式，因此对于右删失数据，它比现有的 WER 方法具有更准确的预测性能。我们的发现得到了广泛的蒙特卡罗模拟研究和实际数据应用的支持。
