Packaged: 2011-10-27 13:41:21 UTC; Georg Heinze

Title: Cox Regression with Firth's Penalized Likelihood
Description: Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression
which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function).
The program fits profile penalized likelihood confidence intervals which were proved to outperform
Packaged: 2016-12-22 10:37:10 UTC; Biometrikus

which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function), see
Heinze and Schemper (2001) and Heinze and Dunkler (2008).
Packaged: 2020-09-21 11:38:11 UTC; Biometrikus

Packaged: 2018-03-03 09:39:29 UTC; Biometrikus

Author: R by Meinhard Ploner,Fortran by Georg Heinze
Description: Weighted estimation for Cox regression
Packaged: Fri May 23 11:39:16 2008; Georg

Packaged: Thu Jun 26 14:30:54 2008; Georg

Packaged: Fri Jun 5 12:54:06 2009; Georg

Packaged: 2011-10-25 09:22:16 UTC; Georg Heinze

Title: Weighted Estimation in Cox Regression
Author: R by Meinhard Ploner, Georg Heinze, Daniela Dunkler, Fortran by Georg Heinze
Description: 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
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.

Authors@R: c(person("Georg", "Heinze", role=c("aut","cre"), email="georg.heinze@meduniwien.ac.at"),
Description: 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
The package provides options to estimate time-dependent effects conveniently by
including interactions of covariates with arbitrary functions of time, with or without
Packaged: 2017-01-02 10:53:56 UTC; Biometrikus