Cindex: compute C index for a Cox model

In glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models

compute C index for a Cox model

Description

Computes Harrel's C index for predictions from a "coxnet" object.

Usage

Cindex(pred, y, weights = rep(1, nrow(y)))

Arguments

pred	Predictions from a "coxnet" object
y	a survival response object - a matrix with two columns "time" and "status"; see documentation for "glmnet"
weights	optional observation weights

Details

Computes the concordance index, taking into account censoring.

Author(s)

Trevor Hastie [e-mail]

References

Harrel Jr, F. E. and Lee, K. L. and Mark, D. B. (1996) Tutorial in biostatistics: multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing error, Statistics in Medicine, 15, pages 361–387.

See Also

cv.glmnet

Examples

set.seed(10101)
N = 1000
p = 30
nzc = p/3
x = matrix(rnorm(N * p), N, p)
beta = rnorm(nzc)
fx = x[, seq(nzc)] %*% beta/3
hx = exp(fx)
ty = rexp(N, hx)
tcens = rbinom(n = N, prob = 0.3, size = 1)  # censoring indicator
y = cbind(time = ty, status = 1 - tcens)  # y=Surv(ty,1-tcens) with library(survival)
fit = glmnet(x, y, family = "cox")
pred = predict(fit, newx = x)
apply(pred, 2, Cindex, y=y)
cv.glmnet(x, y, family = "cox", type.measure = "C")

glmnet documentation built on Aug. 22, 2023, 9:12 a.m.