The semiparametric additive hazards model is an important way for studying the effect of potential risk factors for right-censored time-to-event data. In this paper, we study the additive hazards model in the presence of auxiliary subgroup \(t^*\)-year survival information. We formulate the known auxiliary information in the form of estimating equations, and combine them with the conventional score-type estimating equations for the estimation of the regression parameters based on the maximum empirical likelihood method. We prove that the new estimator of the regression coefficients follows asymptotically a multivariate normal distribution with a sandwich-type covariance matrix that can be consistently estimated, and is strictly more efficient, in an asymptotic sense, than the conventional one without incorporation of the available auxiliary information. Simulation studies show that the new proposal has substantial advantages over the conventional one in terms of standard errors, and with the accommodation of more informative information, the proposed estimator becomes more competing. An AIDS data example is used for illustration.

中文翻译：

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具有辅助亚组生存信息的附加危害模型。

半参数加性危害模型是研究针对右删失的事件发生时间数据的潜在风险因素的影响的重要方法。在本文中，我们研究存在辅助子组\（t ^ * \）时的加性危害模型年生存信息。我们以估计方程的形式制定已知的辅助信息，并将它们与常规的得分类型估计方程相结合，以基于最大经验似然方法估计回归参数。我们证明了回归系数的新估计量渐近地遵循具有正态分布的三明治型协方差矩阵的多元正态分布，在没有渐进意义的情况下，与没有结合可用辅助变量的传统方法相比，该方法在渐近意义上严格地更有效。信息。仿真研究表明，新建议书在标准误差方面比传统建议书有很多优势，并且随着更多信息的提供，建议的估计器变得更具竞争性。