Tong Xingwei, Zhu Liang, Leng Chenlei, Leisenring Wendy, Robison Leslie L
Department of Statistics and Applied Probability, National University of Singapore, Singapore.
Stat Med. 2013 Dec 10;32(28):4980-94. doi: 10.1002/sim.5885. Epub 2013 Jul 3.
We consider a general semiparametric hazards regression model that encompasses the Cox proportional hazards model and the accelerated failure time model for survival analysis. To overcome the nonexistence of the maximum likelihood, we derive a kernel-smoothed profile likelihood function and prove that the resulting estimates of the regression parameters are consistent and achieve semiparametric efficiency. In addition, we develop penalized structure selection techniques to determine which covariates constitute the accelerated failure time model and which covariates constitute the proportional hazards model. The proposed method is able to estimate the model structure consistently and model parameters efficiently. Furthermore, variance estimation is straightforward. The proposed estimation performs well in simulation studies and is applied to the analysis of a real data set.
我们考虑一个通用的半参数风险回归模型，该模型涵盖了用于生存分析的Cox比例风险模型和加速失效时间模型。为了克服最大似然估计不存在的问题，我们推导了一个核平滑轮廓似然函数，并证明所得回归参数估计是一致的且达到半参数效率。此外，我们开发了惩罚结构选择技术，以确定哪些协变量构成加速失效时间模型，哪些协变量构成比例风险模型。所提出的方法能够一致地估计模型结构并有效地估计模型参数。此外，方差估计很简单。所提出的估计方法在模拟研究中表现良好，并应用于一个真实数据集的分析。
