coxr: Fit Robustly Proportional Hazards Regression Model

In coxrobust: Fit Robustly Proportional Hazards Regression Model

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

formula	a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function.
data	a data frame in which to interpret the variables named in the formula, or in the subset.
subset	expression saying that only a subset of the rows of the data should be used in the fit.
na.action	a missing-data filter function, applied to the model.frame, after any subset argument has been used.
trunc	roughly, quantile of the sample T_i exp(β'Z_i), it determines the trimming level for the robust estimator.
f.weight	type of weighting function, default is "quadratic"
singular.ok	logical value indicating how to handle collinearity in the model matrix. If TRUE, the program will automatically skip over columns of the X matrix that are linear combinations of earlier columns. In this case the coefficients for such columns will be NA, and the variance matrix will contain zeros. For ancillary calculations, such as the linear predictor, the missing coefficients are treated as zeros.
model	a logical value indicating whether model frame should be included as a component of the returned value.

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)
}

coxrobust documentation built on April 6, 2022, 5:06 p.m.