Wang Yue, Nan Bin, Kalbfleisch John D
Department of Statistics, University of California, Irvine.
Department of Biostatistics, University of Michigan, Ann Arbor.
J Am Stat Assoc. 2024;119(546):1102-1111. doi: 10.1080/01621459.2023.2169702. Epub 2023 Feb 28.
We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model misspecification. We consider a kernel smoothing method for estimating regression coefficients in our model and use cross-validation for bandwidth selection, applying undersmoothing in the final analysis to eliminate the asymptotic bias of the kernel estimator. We show that the kernel estimates follow a finite-dimensional normal distribution asymptotically under mild regularity conditions, and provide an easily computed sandwich covariance matrix estimator. We conduct extensive simulations that show desirable performance of the proposed approach, and apply the method to analyzing the medical cost data for patients with end-stage renal disease.
我们提出了一种用于纵向测量的非参数双变量时变系数模型，该模型适用于存在右删失的终末事件。时变系数捕捉了协变量效应随随访时间和剩余寿命的纵向轨迹。所提出的模型扩展了近期文献中给定终末事件时间的参数条件方法，从而避免了潜在的模型误设。我们考虑一种核平滑方法来估计模型中的回归系数，并使用交叉验证进行带宽选择，在最终分析中采用欠平滑以消除核估计量的渐近偏差。我们表明，在适度的正则条件下，核估计量渐近地服从有限维正态分布，并提供了一个易于计算的三明治协方差矩阵估计量。我们进行了广泛的模拟，结果表明所提出的方法具有理想的性能，并将该方法应用于分析终末期肾病患者的医疗费用数据。
