For massive survival data, we propose a subsampling algorithm to efficiently approximate the estimates of regression parameters in the additive hazards model. We establish consistency and asymptotic normality of the subsample‐based estimator given the full data. The optimal subsampling probabilities are obtained via minimizing asymptotic variance of the resulting estimator. The subsample‐based procedure can largely reduce the computational cost compared with the full data method. In numerical simulations, our method has low bias and satisfactory coverage probabilities. We provide an illustrative example on the survival analysis of patients with lymphoma cancer from the Surveillance, Epidemiology, and End Results Program.

中文翻译：

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基于加性风险模型的海量生存数据抽样估计

对于海量生存数据，我们提出了一种二次抽样算法来有效地近似加性风险模型中回归参数的估计。在给定完整数据的情况下，我们建立了基于子样本的估计量的一致性和渐近正态性。通过最小化结果估计量的渐近方差来获得最佳子采样概率。与全数据方法相比，基于子样本的过程可以大大降低计算成本。在数值模拟中，我们的方法具有低偏差和令人满意的覆盖概率。我们提供了一个来自监测、流行病学和最终结果计划的淋巴瘤患者生存分析的说明性示例。