The association between two event times is of scientific importance in various fields. Due to population heterogeneity, it is desirable to examine the degree to which local association depends on different characteristics of the population. Here we adopt a novel quantile-based local association measure and propose a conditional quantile association regression model to allow covariate effects on local association of two survival times. Estimating equations for the quantile association coefficients are constructed based on the relationship between this quantile association measure and the conditional copula. Asymptotic properties for the resulting estimators are rigorously derived, and induced smoothing is used to obtain the covariance matrix. Through simulations we demonstrate the good practical performance of the proposed inference procedures. An application to age-related macular degeneration (AMD) data reals interesting varying effects of the baseline AMD severity score on the local association between two AMD progression times.

中文翻译：

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双变量生存数据的分位数关联回归

两个事件时间之间的关联在各个领​​域都具有重要的科学意义。由于人口异质性，需要检查局部关联的程度取决于人口的不同特征。在这里，我们采用了一种新的基于分位数的局部关联度量，并提出了一种条件分位数关联回归模型，以允许对两个生存时间的局部关联产生协变量影响。分位数关联系数的估计方程是基于该分位数关联度量与条件联结之间的关系构建的。所得估计量的渐近特性是严格导出的，并使用诱导平滑来获得协方差矩阵。通过模拟，我们证明了所提出的推理程序的良好实际性能。