# royston: Compute Royston's D for a Cox model In survival: Survival Analysis

## Description

Compute the D statistic proposed by Royston and Sauerbrei along with several pseudo- R square values.

## Usage

 `1` ```royston(fit, newdata, ties = TRUE, adjust = FALSE) ```

## Arguments

 `fit` a coxph fit `newdata` optional validation data set `ties` make a correction for ties in the risk score `adjust` adjust for possible overfitting

## Details

These values are called pseudo R-squared since they involve only the linear predictor, and not the outcome. `R.D` is the value that corresponsds the Royston and Sauerbrei D statistic. `R.KO` is the value proposed by Kent and O'Quigley, `R.N` is the value proposed by Nagelkerke, and `C.GH` corresponds to Goen and Heller's concordance measure.

An adjustment for D is based on the ratio r= (number of events)/(number of coefficients). For models which have sufficient sample size (r>20) the adjustment will be small.

## Value

a vector containing the value of D, the estimated standard error of D, and four pseudo R-squared values.

## References

M. Goen and G. Heller, Concordance probability and discriminatory power in proportional hazards regression. Biometrika 92:965-970, 2005.

N. Nagelkerke, J. Oosting, J. and A. Hart, A simple test for goodness of fit of Cox's proportional hazards model. Biometrics 40:483-486, 1984.

P. Royston and W. Sauerbrei, A new measure of prognostic separation in survival data. Statistics in Medicine 23:723-748, 2004.

## Examples

 ```1 2 3 4 5``` ```# An example used in Royston and Sauerbrei pbc2 <- na.omit(pbc) # no missing values cfit <- coxph(Surv(time, status==2) ~ age + log(bili) + edema + albumin + stage + copper, data=pbc2, ties="breslow") royston(cfit) ```

survival documentation built on Aug. 24, 2021, 5:06 p.m.