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关于在 Cox 回归模型中计算调整风险差和所需治疗人数的渐近置信区间的说明。

A note on calculating asymptotic confidence intervals for the adjusted risk difference and number needed to treat in the Cox regression model.

机构信息

German Cancer Consortium (DKTK), Heidelberg, Germany; Institute of Medical Informatics, Biometry, and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany.

出版信息

Stat Med. 2014 Feb 28;33(5):798-810. doi: 10.1002/sim.5913. Epub 2013 Jul 30.

Abstract

Recently, Laubender and Bender (Stat. Med. 2010; 29: 851-859) applied the average risk difference (RD) approach to estimate adjusted RD and corresponding number needed to treat measures in the Cox proportional hazards model. We calculated standard errors and confidence intervals by using bootstrap techniques. In this paper, we develop asymptotic variance estimates of the adjusted RD measures and corresponding asymptotic confidence intervals within the counting process theory and evaluated them in a simulation study. We illustrate the use of the asymptotic confidence intervals by means of data of the Düsseldorf Obesity Mortality Study.

摘要

最近,Laubender 和 Bender(Stat. Med. 2010; 29: 851-859)应用平均风险差异(RD)方法来估计 Cox 比例风险模型中的调整 RD 和相应的需要治疗人数。我们使用 bootstrap 技术计算标准误差和置信区间。在本文中,我们在计数过程理论中开发了调整 RD 度量的渐近方差估计值和相应的渐近置信区间,并在模拟研究中对其进行了评估。我们通过杜塞尔多夫肥胖死亡率研究的数据说明了渐近置信区间的使用。

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