A Spline-Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval-Censored Data
A Spline-Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval-Censored Data
复制标题
DOI：
10.1111/j.1467-9469.2009.00680.x
复制
发表时间：
2010-06-01
影响因子：
1
通讯作者：
Huang, Jian
中科院分区：
文献类型：
--
作者：
Zhang, Ying;Hua, Lei;Huang, Jian
我们提出了一种基于样条基的半参数极大似然方法来分析具有区间截尾数据的Cox模型。在该方法中，基线累积风险函数用单调B-样条函数逼近。我们推广了广义Rosen算法来计算最大似然估计。我们证明了回归参数的估计是渐近正态的和半参数有效的，尽管基线累积风险函数的估计以比根-n慢的速度收敛。我们还发展了一种易于实现的方法来一致地估计估计的回归参数的标准误差，这使得所提出的区间删失数据的Cox模型的推断过程变得容易。所提出的方法通过模拟研究对其有限样本性能进行了评估，并使用乳房美容研究的数据进行了说明。
We propose a spline-based semiparametric maximum likelihood approach to analysing the Cox model with interval-censored data. With this approach, the baseline cumulative hazard function is approximated by a monotone B-spline function. We extend the generalized Rosen algorithm to compute the maximum likelihood estimate. We show that the estimator of the regression parameter is asymptotically normal and semiparametrically efficient, although the estimator of the baseline cumulative hazard function converges at a rate slower than root-n. We also develop an easy-to-implement method for consistently estimating the standard error of the estimated regression parameter, which facilitates the proposed inference procedure for the Cox model with interval-censored data. The proposed method is evaluated by simulation studies regarding its finite sample performance and is illustrated using data from a breast cosmesis study.