具有不同数量协变量的 Cox 比例危害模型的统计推断。

Pub Date : 2023-06-01 Epub Date: 2022-04-25 DOI:10.1111/sjos.12595
Lu Xia, Bin Nan, Yi Li
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引用次数: 0

摘要

对于具有不同数量协变量的回归模型的统计推断,现有文献通常对费雪信息矩阵的逆矩阵做出稀疏性假设。然而,这种假设在 Cox 比例危险模型中经常被违反,从而导致有偏差的估计值和覆盖不足的置信区间。我们提出了一种改进的debiased lasso方法,它可以解决一系列二次编程问题,在不提出稀疏矩阵假设的情况下逼近逆信息矩阵。当协变量的维数随样本量的增加而发散时,我们建立了估计回归系数的渐近结果。大量的模拟证明,我们提出的方法能提供一致的估计值和置信区间,并具有名义覆盖概率。通过波士顿肺癌生存队列(Boston Lung Cancer Survival Cohort)评估遗传标记对患者总生存期的影响,进一步证明了该方法的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical Inference for Cox Proportional Hazards Models with a Diverging Number of Covariates.

For statistical inference on regression models with a diverging number of covariates, the existing literature typically makes sparsity assumptions on the inverse of the Fisher information matrix. Such assumptions, however, are often violated under Cox proportion hazards models, leading to biased estimates with under-coverage confidence intervals. We propose a modified debiased lasso method, which solves a series of quadratic programming problems to approximate the inverse information matrix without posing sparse matrix assumptions. We establish asymptotic results for the estimated regression coefficients when the dimension of covariates diverges with the sample size. As demonstrated by extensive simulations, our proposed method provides consistent estimates and confidence intervals with nominal coverage probabilities. The utility of the method is further demonstrated by assessing the effects of genetic markers on patients' overall survival with the Boston Lung Cancer Survival Cohort, a large-scale epidemiology study investigating mechanisms underlying the lung cancer.

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