论文标题
添加危害模型中的最大似然估计
Maximum likelihood estimation in the additive hazards model
论文作者
论文摘要
与流行的COX模型相比，加性危害模型以添加剂方式指定了协变量对危害的影响。作为非参数模型，它提供了一种非常灵活的方式来建模时变的协变量效应。它最常见的是普通最小二乘。在本文中，我们考虑了协变量有界的情况，并在其域中所有协变量的危险中得出了最大似然估计量。我们描述了一种有效的算法，以找到最大似然估计器。该方法与模拟研究中普通最小二乘方法形成对比，并且该方法在现实的数据集上进行了说明。
The additive hazards model specifies the effect of covariates on the hazard in an additive way, in contrast to the popular Cox model, in which it is multiplicative. As non-parametric model, it offers a very flexible way of modeling time-varying covariate effects. It is most commonly estimated by ordinary least squares. In this paper we consider the case where covariates are bounded, and derive the maximum likelihood estimator under the constraint that the hazard is non-negative for all covariate values in their domain. We describe an efficient algorithm to find the maximum likelihood estimator. The method is contrasted with the ordinary least squares approach in a simulation study, and the method is illustrated on a realistic data set.
