hdnom (version 5.0)

hdnom.external.calibrate: Externally Calibrate High-Dimensional Cox Models

Description

Externally Calibrate High-Dimensional Cox Models

Usage

hdnom.external.calibrate(object, x, time, event, x_new, time_new, event_new,
  pred.at, ngroup = 5)

Arguments

object

Model object fitted by hdcox.*() functions.

x

Matrix of training data used for fitting the model.

time

Survival time of the training data. Must be of the same length with the number of rows as x.

event

Status indicator of the training data, normally 0 = alive, 1 = dead. Must be of the same length with the number of rows as x.

x_new

Matrix of predictors for the external validation data.

time_new

Survival time of the external validation data. Must be of the same length with the number of rows as x_new.

event_new

Status indicator of the external validation data, normally 0 = alive, 1 = dead. Must be of the same length with the number of rows as x_new.

pred.at

Time point at which external calibration should take place.

ngroup

Number of groups to be formed for external calibration.

Examples

# NOT RUN {
library("survival")

# Load imputed SMART data
data(smart)
# Use the first 1000 samples as training data
# (the data used for internal validation)
x = as.matrix(smart[, -c(1, 2)])[1:1000, ]
time = smart$TEVENT[1:1000]
event = smart$EVENT[1:1000]

# Take the next 1000 samples as external calibration data
# In practice, usually use data collected in other studies
x_new = as.matrix(smart[, -c(1, 2)])[1001:2000, ]
time_new = smart$TEVENT[1001:2000]
event_new = smart$EVENT[1001:2000]

# Fit Cox model with lasso penalty
fit = hdcox.lasso(
  x, Surv(time, event),
  nfolds = 5, rule = "lambda.1se", seed = 11)

# External calibration
cal.ext = hdnom.external.calibrate(
  fit, x, time, event,
  x_new, time_new, event_new,
  pred.at = 365 * 5, ngroup = 5)

print(cal.ext)
summary(cal.ext)
plot(cal.ext, xlim = c(0.6, 1), ylim = c(0.6, 1))
# }