Local linear estimation for time-dependent coefficients in Cox's regression models
Local linear estimation for time-dependent coefficients in Cox's regression models
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DOI：
10.1111/1467-9469.00320
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发表时间：
2003-03-01
影响因子：
1
通讯作者：
Sun, YQ
中科院分区：
文献类型：
--
作者：
Cai, ZW;Sun, YQ
本文提出了一种局部偏似然估计方法来估计考克斯回归模型中的时变系数。其基本思想是在散点图平滑中使用的局部线性拟合技术的简单扩展。基于每个时间点周围的窗口中的部分似然来局部估计系数。多个时间依赖性协变量被纳入局部偏似然程序。该程序是有用的诊断工具，并可用于揭示时间依赖性或偏离比例风险模型。局部偏似然估计的程序设计相对简单，只需对比例风险模型的现有程序进行少量修改即可。由此产生的估计的渐近性质的建立和比较与当地常数拟合。本文还提出了渐近方差的一个相合估计。通过对胃癌患者的真实的数据集的研究，说明了这种方法，并提出了一个模拟研究。
This article develops a local partial likelihood technique to estimate the time-dependent coefficients in Cox's regression model. The basic idea is a simple extension of the local linear fitting technique used,in the scatterplot smoothing. The coefficients are estimated locally based on the partial likelihood in a window around each time point. Multiple time-dependent covariates are incorporated in the local partial likelihood procedure. The procedure is useful as a diagnostic tool and can be used in uncovering time-dependencies or departure from the proportional hazards model. The programming involved in the local partial likelihood estimation is relatively simple and it can be modified with few efforts from the existing programs for the proportional hazards model. The asymptotic properties of the resulting estimator are established and compared with those from the local constant fitting. A consistent estimator of the asymptotic variance is also proposed. The approach is illustrated by a real data set from the study of gastric cancer patients and a simulation study is also presented.