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.
Packaged: 2018-04-17 12:25:14 UTC; Biometrician

person("Daniela", "Dunkler", email="daniela.dunkler@meduniwien.ac.at", role=c("aut", "cre")),
Packaged: 2020-06-22 07:38:56 UTC; Biometrician

Title: Fit Robustly Proportional Hazards Regression Model
Description: An implementation of robust estimation in Cox model. Functionality includes fitting efficiently and robustly Cox proportional hazards regression model in its basic form, where explanatory variables are time independent with one event per subject.  Method is based on a smooth modification of the partial likelihood.
Packaged: 2022-04-06 12:47:32 UTC; shanascogin

Description: Fit robustly proportional hazards regression model
Author: Tadeusz Bednarski <t.bednarski@prawo.uni.wroc.pl>, Filip

Title: Cox Proportional Hazards Regression for Right Truncated Data
Author: Bella Vakulenko-Lagun, Micha Mandel, Rebecca A. Betensky
Description: Fits Cox regression based on retrospectively ascertained times-to-event. The method uses Inverse-Probability-Weighting estimating equations.
Imports: survival, BB, inline, gss, ggplot2

Title: Cox Proportional Hazards Regression for Right-Truncated Data

Imports: survival, BB, inline, gss, ggplot2, Rcpp

Description: It fits a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.

Description: Fit a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.

Author: Quentin Giai Gianetto, Florence Combes, Yohann Couté, Christophe Bruley, Thomas Burger
Maintainer: Quentin Giai Gianetto <quentin2g@yahoo.fr>
Description: Functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.