论文标题
对双Cox模型中估计质量的模拟研究，具有共同的脆弱性，用于非比例危害生存分析
A simulation study of the estimation quality in the double-Cox model with shared frailty for non-proportional hazards survival analysis
论文作者
论文摘要
Cox回归是一种半参数生存分析方法，在生物医学应用中非常受欢迎。比例危害假设是COX模型中的关键要求。为了适应非比例危害，我们建议使用依赖于协变量的向量的其他独立的COX-回归项来参数基线危害函数的形状参数。我们将此模型称为双Cox模型。用于拟合双Cox模型的R程序可在GitHub上获得。我们正式介绍了具有共同脆弱的双Cox模型，并通过模拟调查了Gompertz和Weibull基线危害的估计偏差和覆盖范围。在较弱的差异和大量簇的应用中，边际似然估计几乎是无偏见的，基于轮廓的基于轮廓的置信区间为所有模型参数提供了良好的覆盖范围。我们还将过度拟合的双Cox模型与在仅比例比例危害的情况下的标准COX模型的结果进行比较。我们对模型参数的偏差和覆盖范围的模拟结果在12个表中提供，在145个A4图中，总共178页。
The Cox regression, a semi-parametric method of survival analysis, is extremely popular in biomedical applications. The proportional hazards assumption is a key requirement in the Cox model. To accommodate non-proportional hazards, we propose to parameterise the shape parameter of the baseline hazard function using the additional, separate Cox-regression term which depends on the vector of the covariates. We call this model the double-Cox model. The R programs for fitting the double-Cox model are available on Github. We formally introduce the double-Cox model with shared frailty and investigate, by simulation, the estimation bias and the coverage of the proposed point and interval estimation methods for the Gompertz and the Weibull baseline hazards. In applications with low frailty variance and a large number of clusters, the marginal likelihood estimation is almost unbiased and the profile likelihood-based confidence intervals provide good coverage for all model parameters. We also compare the results from the over-fitted double-Cox model to those from the standard Cox model with frailty in the case of the scale-only proportional hazards. Results of our simulations on the bias and coverage of the model parameters are provided in 12 Tables and in 145 A4 Figures, 178 pages in total.
