# concord

0th

Percentile

##### Compute Generalized Concordance Probabilities for Objects of Class coxphw or coxph

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

Keywords
survival
##### 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.

##### 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.

coxphw

• concord
##### Examples
# NOT RUN {
data("gastric")
fit <- coxphw(Surv(time, status) ~ radiation, data = gastric, template = "AHR")
concord(fit)
# }
Documentation reproduced from package coxphw, version 4.0.1, License: GPL-2

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