@article{JSSv084i02,
title={Weighted Cox Regression Using the R Package coxphw},
volume={84},
url={https://www.jstatsoft.org/index.php/jss/article/view/v084i02},
doi={10.18637/jss.v084.i02},
abstract={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.},
number={2},
journal={Journal of Statistical Software},
author={Dunkler, Daniela and Ploner, Meinhard and Schemper, Michael and Heinze, Georg},
year={2018},
pages={1–26}
}