coxphw: Weighted Estimation in Cox Regression
This package implements weighted estimation in Cox regression as proposed by
Schemper, Wakounig and Heinze (Statistics in Medicine, 2009). Weighted Cox regression
provides unbiased average hazard ratio estimates also in case of non-proportional hazards.
Approximated generalized concordance probability an effect size measure for clear-cut
decisions can be obtained.
Additionally estimation of nonlinear effects using fractional polynomials similar to the
MFP algorithm (Royston, Sauerbrei, 2008) is provided. This feature can also be used to
estimate the interaction of a covariate with a nonlinear function of time.
||R (≥ 3.0.2), survival|
||R by Meinhard Ploner, Georg Heinze, Daniela Dunkler, Fortran by Georg Heinze|
||Georg Heinze <georg.heinze at meduniwien.ac.at>|
||coxphw citation info |