This package implements weighted estimation in Cox regression as proposed by Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, tools:::Rd_expr_doi("10.1002/sim.3623")). Weighted Cox regression provides unbiased average hazard ratio estimates also in case of non-proportional hazards. The package provides options to estimate time-dependent 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, tools:::Rd_expr_doi("10.18637/jss.v084.i02")).
Georg Heinze, Meinhard Ploner, Daniela Dunkler
Maintainer: daniela.dunkler@meduniwien.ac.at
| Package: | coxphw |
| Type: | Package |
| Version: | 4.0.2 |
| Date: | 2020-06-16 |
| License: | GPL-2 |
Main functions included in the coxphw package are
coxphw | weighted 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 time-dependent 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 chi-squared test statistics and p-values for one or more regression |
| coefficients given their variance-covariance matrix. |
Data sets included in the coxphw package are
biofeedback | biofeedback treatment data |
gastric | gastric cancer data |
Dunkler D, Ploner M, Schemper M, Heinze G. (2018) Weighted Cox Regression Using the R Package coxphw. JSS 84, 1--26, tools:::Rd_expr_doi("10.18637/jss.v084.i02").
Dunkler D, Schemper M, Heinze G. (2010) Gene Selection in Microarray Survival Studies Under Possibly Non-Proportional Hazards. Bioinformatics 26:784-90.
Lin D and Wei L (1989). The Robust Inference for the Cox Proportional Hazards Model. J AM STAT ASSOC 84, 1074-1078.
Lin D (1991). Goodness-of-Fit Analysis for the Cox Regression Model Based on a Class of Parameter Estimators. J AM STAT ASSOC 86, 725-728.
Royston P and Altman D (1994). Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling. J R STAT SOC C-APPL 43, 429-467.
Royston P and Sauerbrei W (2008). Multivariable Model-Building. 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, 144-152.
Schemper M (1992). Cox Analysis of Survival Data with Non-Proportional Hazard Functions. J R STAT SOC D 41, 455-465.
Schemper M, Wakounig S and Heinze G (2009). The Estimation of Average Hazard Ratios by Weighted Cox Regression. Stat Med 28, 2473-2489, tools:::Rd_expr_doi("10.1002/sim.3623").
Xu R and O'Quigley J (2000). Estimating Average Regression Effect Under Non-Proportional Hazards. Biostatistics 1, 423-439.
coxphw, concord, plot.coxphw, predict.coxphw, plot.coxphw.predict, wald