Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Weighted Cox Regression Using the R Package coxphw | Dunkler | Journal of Statistical Software
Authors: Daniela Dunkler, Meinhard Ploner, Michael Schemper, Georg Heinze
Title: Weighted Cox Regression Using the R Package coxphw
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.

Page views:: 891. Submitted: 2015-12-23. Published: 2018-04-17.
Paper: Weighted Cox Regression Using the R Package coxphw     Download PDF (Downloads: 379)
coxphw_4.0.1.tar.gz: R source package Download (Downloads: 20; 181KB)
v84i02.R: R replication code Download (Downloads: 42; 32KB)

DOI: 10.18637/jss.v084.i02

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.