# concord

##### 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.

##### See Also

##### 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*