The additive hazards model is one of the most popular regression models for analyzing failure time data, especially when one is interested in the excess risk or risk difference. Although a couple of methods have been developed in the literature for regression analysis of interval-censored data, a general type of failure time data, they may be complicated or inefficient. Corresponding to this, we present a new maximum likelihood estimation procedure based on the sieve approach and in particular, develop an EM algorithm that involves a two-stage data augmentation with the use of Poisson latent variables. The method can be easily implemented and the asymptotic properties of the proposed estimators are established. A simulation study is conducted to assess the performance of the proposed method and indicates that it works well for practical situations. Also the method is applied to a set of interval-censored data from an AIDS cohort study.

加性危害模型是用于分析故障时间数据的最受欢迎的回归模型之一，尤其是当人们对超额风险或风险差异感兴趣时。尽管文献中已经开发了两种方法来对间隔检查的数据（故障时间数据的一般类型）进行回归分析，但是它们可能很复杂或效率很低。与此对应，我们提出了一种基于筛子方法的新的最大似然估计程序，尤其是开发了一种使用泊松潜变量的涉及两阶段数据扩充的EM算法。该方法可以轻松实现，并建立了所提出估计量的渐近性质。进行了仿真研究，以评估该方法的性能，并表明该方法在实际情况下效果很好。