Choi Sangbum, Huang Xuelin, Chen Yi-Hau
Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1411, Houston, TX, 77030, USA,
Lifetime Data Anal. 2014 Jul;20(3):369-86. doi: 10.1007/s10985-013-9268-2. Epub 2013 Jun 13.
We propose a new class of semiparametric regression models based on a multiplicative frailty assumption with a discrete frailty, which may account for cured subgroup in population. The cure model framework is then recast as a problem with a transformation model. The proposed models can explain a broad range of nonproportional hazards structures along with a cured proportion. An efficient and simple algorithm based on the martingale process is developed to locate the nonparametric maximum likelihood estimator. Unlike existing expectation-maximization based methods, our approach directly maximizes a nonparametric likelihood function, and the calculation of consistent variance estimates is immediate. The proposed method is useful for resolving identifiability features embedded in semiparametric cure models. Simulation studies are presented to demonstrate the finite sample properties of the proposed method. A case study of stage III soft-tissue sarcoma is given as an illustration.
我们基于具有离散脆弱性的乘法脆弱性假设提出了一类新的半参数回归模型，该假设可能解释总体中的治愈亚组。然后将治愈模型框架重塑为一个变换模型问题。所提出的模型可以解释广泛的非比例风险结构以及治愈比例。开发了一种基于鞅过程的高效且简单的算法来定位非参数最大似然估计器。与现有的基于期望最大化的方法不同，我们的方法直接最大化非参数似然函数，并且一致方差估计的计算很直接。所提出的方法对于解决半参数治愈模型中嵌入的可识别性特征很有用。进行了模拟研究以证明所提出方法的有限样本性质。给出了III期软组织肉瘤的案例研究作为说明。
