Type:	Package
Title:	Fit Robustly Proportional Hazards Regression Model
Version:	1.0.1
Date:	2022-04-02
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.
URL:	https://github.com/ShanaScogin/coxrobust
BugReports:	https://github.com/ShanaScogin/coxrobust/issues
License:	GPL-3
NeedsCompilation:	yes
Imports:	survival
Depends:	R (≥ 2.0.0)
Suggests:	testthat (≥ 3.0.0), knitr
Encoding:	UTF-8
LazyLoad:	TRUE
RoxygenNote:	7.1.2
LinkingTo:	Rcpp, RcppArmadillo
Config/testthat/edition:	3
Packaged:	2022-04-06 12:47:32 UTC; shanascogin
Author:	Tadeusz Bednarski [aut], Filip Borowicz [aut], Shana Scogin [cre]
Maintainer:	Shana Scogin <shanarscogin@gmail.com>
Repository:	CRAN
Date/Publication:	2022-04-06 14:02:33 UTC

Fit Robustly Proportional Hazards Regression Model

Description

Fits 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.

Usage

coxr(
  formula,
  data,
  subset,
  na.action,
  trunc = 0.95,
  f.weight = c("linear", "quadratic", "exponential"),
  singular.ok = TRUE,
  model = FALSE
)

Arguments

Value

a data frame containing MCMC summary statistics.An object of class coxr. See coxr.object for details.

References

Bednarski, T. (1993). Robust estimation in Cox's regression model. Scandinavian Journal of Statistics. Vol. 20, 213–225.

Bednarski, T. (1989). On sensitivity of Cox's estimator. Statistics and Decisions. 7, 215–228.

Grzegorek, K.(1993). On robust estimation of baseline hazard under the Cox model and via Frechet differentiability. Preprint of the Institute of Mathematics of the Polish Academy of Sciences.518.

Minder, C.E. & Bednarski, T. (1996). A robust method for proportional hazards regression. Statistics in Medicine Vol. 15, 1033–1047.

Examples

if (interactive()) {
# Create a simple test data set using the attached function gen_data
a <- gen_data(200, c(1, 0.1, 2), cont = 0.05, p.censor = 0.30)
result <- coxr(Surv(time, status) ~ X1 + X2 + X3, data = a , trunc = 0.9)
result
plot(result)
}

Fit Robustly Proportional Hazards Regression Object

Description

This class of objects is returned by coxr function to represent efficiently and robustly fitted proportional hazards regression model. Objects of this class have methods for the functions print, plot and predict.

Value

The following components must be included in a legitimate coxr object.

The object also contain the following, for documentation see the lm object: terms, call, x, y and optionally model.

See Also

coxr

coxrobust Overview

Description

This package currently has one main function that fits a robustly proportional hazards regression model

Main Functions

• coxr()

• gen_data()

• plot.coxr()

• predict.coxr()

Generate Data from the Proportional Hazards Regression Model

Description

Generates data set from the proportional hazards regression model without or with contamination.

Usage

gen_data(n, beta, cont = 0, p.censor = 0)

Arguments

Value

Data frame containing the following variables:

• timevector of survival times.

• statusvector of censoring status.

• X1, X2, ...explanatory variables (their number is determined by the dimension of vector of regression coefficients).

Examples

if (interactive()) {
gen_data(50, c(2,-2), cont = 0.05)
}

Plot Diagnostics for a coxr Object

Description

Graphical tool which in a series of 5 graphs let us compare how well data are explained by the estimated proportional hazards model with non-robust (black color) and robust method (green color). The first graph gives standardized difference of two estimated survival functions; one via the Cox model and the other via Kaplan Meier estimator. The following four graphs show the same differences for four strata, defined by the quartiles of the estimated linear predictor. Comparison of estimation results along with analysis of the graphs leads frequently to a very detailed information about the model fit (see examples).

Usage

## S3 method for class 'coxr'
plot(
  x,
  caption = c("Full data set", "First quartile", "Second quartile", "Third quartile",
    "Fourth quartile"),
  main = NULL,
  xlab = "log time",
  ylab = "standardized survival differences",
  ...,
  color = TRUE
)

Arguments

Value

Data frame containing the following variables:

• timevector of survival times.

• statusvector of censoring status.

• X1, X2, ...explanatory variables (their number is determined by the dimension of vector of regression coefficients).