Fitting additive risk models using auxiliary information
Fitting additive risk models using auxiliary information
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DOI：
10.1002/sim.9649
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发表时间：
2023-01
影响因子：
2
通讯作者：
Jie Ding;Jialiang Li;Yang Han;I. McKeague;Xiaoguang Wang
中科院分区：
文献类型：
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作者：
Jie Ding;Jialiang Li;Yang Han;I. McKeague;Xiaoguang Wang
人们越来越有兴趣将外部研究的辅助摘要信息纳入内部个人数据的分析中。在本文中，我们提出了一种用于加性风险模型的自适应估计程序，以通过矩量技术的惩罚方法整合辅助子组生存信息。我们的方法可以容纳来自异构数据的信息。我们的框架中引入了量化内部数据和外部辅助信息之间潜在不可比性程度的参数，而这些参数的非零分量表明违反了同质性假设。我们进一步开发了一种有效的计算算法，通过分析干扰参数来解决数值优化问题。从渐近意义上讲，我们的方法可以非常高效，就好像所有无与伦比的辅助信息都被准确地确认并自动排除在考虑范围之外一样。建立了所提出的回归系数估计量的渐近正态性，并具有可以从数据中一致估计的渐近方差-协方差矩阵的显式公式。仿真研究表明，与仅使用内部数据的传统方法相比，所提出的方法在统计效率上有了显着的提高，并且当给定的辅助生存信息不可比较时减少了估计偏差。我们通过肺癌生存研究来说明所提出的方法。
There has been a growing interest in incorporating auxiliary summary information from external studies into the analysis of internal individual‐level data. In this paper, we propose an adaptive estimation procedure for an additive risk model to integrate auxiliary subgroup survival information via a penalized method of moments technique. Our approach can accommodate information from heterogeneous data. Parameters to quantify the magnitude of potential incomparability between internal data and external auxiliary information are introduced in our framework while nonzero components of these parameters suggest a violation of the homogeneity assumption. We further develop an efficient computational algorithm to solve the numerical optimization problem by profiling out the nuisance parameters. In an asymptotic sense, our method can be as efficient as if all the incomparable auxiliary information is accurately acknowledged and has been automatically excluded from consideration. The asymptotic normality of the proposed estimator of the regression coefficients is established, with an explicit formula for the asymptotic variance‐covariance matrix that can be consistently estimated from the data. Simulation studies show that the proposed method yields a substantial gain in statistical efficiency over the conventional method using the internal data only, and reduces estimation biases when the given auxiliary survival information is incomparable. We illustrate the proposed method with a lung cancer survival study.