TY - JOUR
AU - Dunkler, Daniela
AU - Ploner, Meinhard
AU - Schemper, Michael
AU - Heinze, Georg
PY - 2018/04/17
Y2 - 2022/01/29
TI - Weighted Cox Regression Using the R Package coxphw
JF - Journal of Statistical Software
JA - J. Stat. Soft.
VL - 84
IS - 2
SE - Articles
DO - 10.18637/jss.v084.i02
UR - https://www.jstatsoft.org/index.php/jss/article/view/v084i02
SP - 1 - 26
AB - 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.
ER -