Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, USA.
Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA, USA.
Stat Methods Med Res. 2021 Nov;30(11):2447-2458. doi: 10.1177/09622802211037076. Epub 2021 Sep 14.
Non-proportional hazards data are routinely encountered in randomized clinical trials. In such cases, classic Cox proportional hazards model can suffer from severe power loss, with difficulty in interpretation of the estimated hazard ratio since the treatment effect varies over time. We propose CauchyCP, an omnibus test of change-point Cox regression models, to overcome both challenges while detecting signals of non-proportional hazards patterns. Extensive simulation studies demonstrate that, compared to existing treatment comparison tests under non-proportional hazards, the proposed CauchyCP test (a) controls the type I error better at small levels (); (b) increases the power of detecting time-varying effects; and (c) is more computationally efficient than popular methods like MaxCombo for large-scale data analysis. The superior performance of CauchyCP is further illustrated using retrospective analyses of two randomized clinical trial datasets and a pharmacogenetic biomarker study dataset. The R package is publicly available on CRAN.
在随机临床试验中，经常会遇到非比例风险数据。在这种情况下，经典的 Cox 比例风险模型可能会严重丧失效力，并且由于治疗效果随时间变化，估计的风险比也难以解释。我们提出了 CauchyCP，这是一种用于检测非比例风险模式信号的全面变化点 Cox 回归模型检验方法，可以克服这两个挑战。广泛的模拟研究表明，与非比例风险下现有的治疗比较检验相比，所提出的 CauchyCP 检验 (a) 在小水平 () 下更好地控制了Ⅰ类错误；(b) 提高了检测时变效应的功效；(c) 在大规模数据分析方面比 MaxCombo 等流行方法更具计算效率。通过对两个随机临床试验数据集和一个药物基因组学生物标志物研究数据集的回顾性分析，进一步说明了 CauchyCP 的优越性能。该 R 包可在 CRAN 上公开获取。
