coxphwpackage {coxphw}  R Documentation 
This package implements weighted estimation in Cox regression as proposed by Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, doi: 10.1002/sim.3623). Weighted Cox regression provides unbiased average hazard ratio estimates also in case of nonproportional hazards. The package provides options to estimate timedependent effects conveniently by including interactions of covariates with arbitrary functions of time, with or without making use of the weighting option. For more details we refer to Dunkler, Ploner, Schemper and Heinze (Journal of Statistical Software, 2018, doi: 10.18637/jss.v084.i02).
Package:  coxphw 
Type:  Package 
Version:  4.0.2 
Date:  20200616 
License:  GPL2 
Main functions included in the coxphw package are
coxphw  weigthed estimation of Cox regression: either (recommended) estimation of 
average hazard ratios (Schemper et al., 2009), estimation of average regression  
effects (Xu and O'Quigley, 2000), or proportional hazards regression.  
plot  plots the weights used in a weighted Cox regression analysis against time. 
concord  obtains generalized concordance probabilities with confidence intervalls. 
predict  obtains the effect estimates (of e.g. a nonlinear or a timedependent effect) 
at specified values of a continuous covariable. With plot.coxphw.predict 

these relative or log relative hazard versus values of the continuous covariable  
can be plotted.  
wald  obtain Wald chisquared test statistics and pvalues for one or more regression 
coefficients given their variancecovariance matrix.  
Data sets included in the coxphw package are
biofeedback  biofeedback treatment data 
gastric  gastric cancer data 
The SAS
macro WCM
with similar functionality can be obtained at
http://cemsiis.meduniwien.ac.at/en/kb/scienceresearch/software/statisticalsoftware/wcmcoxphw/.
Important version changes:
Up to Version 2.13 coxphw used a slightly different syntax (arguments: AHR
, AHR.norobust
,
ARE
, PH
, normalize
, censcorr
, prentice
, breslow
,
taroneware
).
From Version 3.0.0 on the old syntax is disabled.
From Version 4.0.0 fractional polynomials are disabled and plotshape
is replaced with predict
and plot.coxphw.predict
.
Georg Heinze, Meinhard Ploner, Daniela Dunkler
Maintainer: daniela.dunkler@meduniwien.ac.at
Dunkler D, Ploner M, Schemper M, Heinze G. (2018) Weighted Cox Regression Using the R Package coxphw. JSS 84, 1–26, doi: 10.18637/jss.v084.i02.
Dunkler D, Schemper M, Heinze G. (2010) Gene Selection in Microarray Survival Studies Under Possibly NonProportional Hazards. Bioinformatics 26:78490.
Lin D and Wei L (1989). The Robust Inference for the Cox Proportional Hazards Model. J AM STAT ASSOC 84, 10741078.
Lin D (1991). GoodnessofFit Analysis for the Cox Regression Model Based on a Class of Parameter Estimators. J AM STAT ASSOC 86, 725728.
Royston P and Altman D (1994). Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling. J R STAT SOC CAPPL 43, 429467.
Royston P and Sauerbrei W (2008). Multivariable ModelBuilding. A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables. Wiley, Chichester, UK.
Sasieni P (1993). Maximum Weighted Partial Likelihood Estimators for the Cox Model. J AM STAT ASSOC 88, 144152.
Schemper M (1992). Cox Analysis of Survival Data with NonProportional Hazard Functions. J R STAT SOC D 41, 455465.
Schemper M, Wakounig S and Heinze G (2009). The Estimation of Average Hazard Ratios by Weighted Cox Regression. Stat Med 28, 24732489, doi: 10.1002/sim.3623.
Xu R and O'Quigley J (2000). Estimating Average Regression Effect Under NonProportional Hazards. Biostatistics 1, 423439.
coxphw
, concord
, plot.coxphw
, predict.coxphw
, plot.coxphw.predict
, wald
## for examples see coxphw