Weighted Cox Regression Using the R Package coxphw
Weighted Cox Regression Using the R Package coxphw
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DOI：
10.18637/jss.v084.i02
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发表时间：
2018-04-01
影响因子：
5.8
通讯作者：
Heinze, Georg
中科院分区：
文献类型：
--
作者：
Dunkler, Daniela;Ploner, Meinhard;Heinze, Georg
考克斯用于生存数据分析的回归模型依赖于比例风险假设。然而，这一假设在实践中经常被违反，因此平均相对风险可能被低估或高估。COX回归的加权估计是一种简约的选择，在非比例风险的情况下也提供了很好的可解释的平均效应。通过两个生物医学实例，说明了存在非比例风险时的适当分析，并讨论了加权COX回归的优点。此外，使用COXPHW软件包，通过包含具有任意时间函数的协变量的交互作用，可以方便地估计时间依赖效应。
Cox's regression model for the analysis of survival data relies on the proportional hazards assumption. However, this assumption is often violated in practice and as a consequence the average relative risk may be under-or overestimated. Weighted estimation of Cox regression is a parsimonious alternative which supplies well interpretable average effects also in case of non-proportional hazards.We provide the R package coxphw implementing weighted Cox regression. By means of two biomedical examples appropriate analyses in the presence of non-proportional hazards are exemplified and advantages of weighted Cox regression are discussed. Moreover, using package coxphw, time-dependent effects can be conveniently estimated by including interactions of covariates with arbitrary functions of time.