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Statistical power to detect violation of the proportional hazards assumption when using the Cox regression model
Journal of Statistical Computation and Simulation ( IF 1.2 ) Pub Date : 2017-11-10 , DOI: 10.1080/00949655.2017.1397151
Peter C Austin 1, 2, 3
Affiliation  

ABSTRACT The use of the Cox proportional hazards regression model is wide-spread. A key assumption of the model is that of proportional hazards. Analysts frequently test the validity of this assumption using statistical significance testing. However, the statistical power of such assessments is frequently unknown. We used Monte Carlo simulations to estimate the statistical power of two different methods for detecting violations of this assumption. When the covariate was binary, we found that a model-based method had greater power than a method based on cumulative sums of martingale residuals. Furthermore, the parametric nature of the distribution of event times had an impact on power when the covariate was binary. Statistical power to detect a strong violation of the proportional hazards assumption was low to moderate even when the number of observed events was high. In many data sets, power to detect a violation of this assumption is likely to be low to modest.

中文翻译:

使用 Cox 回归模型时检测违反比例风险假设的统计能力

摘要 Cox 比例风险回归模型的使用是广泛的。该模型的一个关键假设是比例风险。分析师经常使用统计显着性检验来检验这一假设的有效性。然而,此类评估的统计功效通常是未知的。我们使用蒙特卡罗模拟来估计两种不同方法的统计功效,以检测违反此假设的情况。当协变量为二元时,我们发现基于模型的方法比基于鞅残差累积总和的方法具有更大的功效。此外,当协变量为二元时,事件时间分布的参数性质对功效有影响。即使观察到的事件数量很多,检测严重违反比例风险假设的统计能力也是低到中等的。在许多数据集中,检测违反此假设的能力可能很低。
更新日期:2017-11-10
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