R by Meinhard Ploner,Fortran by Georg Heinze

R by Meinhard Ploner, Georg Heinze, Daniela Dunkler, Fortran by Georg Heinze
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.

c(person("Georg", "Heinze", role=c("aut","cre"), email="georg.heinze@meduniwien.ac.at"),
person("Meinhard", "Ploner", role="aut",),
person("Daniela", "Dunkler", role="aut"))
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 non-proportional hazards.
Approximated generalized concordance probability an effect size measure for clear-cut
decisions can be obtained.
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.

Implements weighted estimation in Cox regression as proposed by
Schemper, Wakounig and Heinze (Statistics in Medicine, 2009,
<doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and
Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>).
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.
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.