concord: Compute Generalized Concordance Probabilities for Objects of...

In coxphw: Weighted Estimation in Cox Regression

Compute Generalized Concordance Probabilities for Objects of Class coxphw or coxph

Description

Compute generalized concordance probabilities with accompanying confidence intervals for objects of class coxphw or coxph.

Usage

 concord(fit, digits = 4) 

Arguments

fit	an object of class coxphw.
digits	integer indicating the number of decimal places to be used. Default is 4.

Details

The generalized concordance probability is defined as P(T_i < T_j | x_i = x_j + 1) with T_i and T_j as survival times of randomly chosen subjects with covariate values x_i and x_j, respectively. Assuming that x_i and x_j are 1 and 0, respectively, this definition includes a two-group comparison.

If proportional hazards can be assumed, the generalized concordance probability can also be derived from Cox proportional hazards regression (coxphw with template = "PH" or coxph) or weighted Cox regression as suggested by Xu and O'Quigley (2000) (coxphw with template = "ARE").

If in a fit to coxphw the betafix argument was used, then for the fixed parameters only the point estimates are given.

Value

A matrix with estimates of the generalized concordance probability with accompanying confidence intervalls for each explanatory variable in the model.

Author(s)

Daniela Dunkler

References

Dunkler D, Schemper M, Heinze G. (2010) Gene Selection in Microarray Survival Studies Under Possibly Non-Proportional Hazards. Bioinformatics 26:784-90.

Xu R and O'Quigley J (2000). Estimating Average Regression Effect Under Non-Proportional Hazards. Biostatistics 1, 423-439.

See Also

coxphw

Examples

data("gastric")
fit <- coxphw(Surv(time, status) ~ radiation, data = gastric, template = "AHR")
concord(fit)

coxphw documentation built on May 29, 2024, 3:18 a.m.