calibrate_external: Externally calibrate high-dimensional Cox models

In road2stat/hdnom: Benchmarking and Visualization Toolkit for Penalized Cox Models

Externally calibrate high-dimensional Cox models

Description

Externally calibrate high-dimensional Cox models

Usage

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

Arguments

object	Model object fitted by hdnom::fit_*().
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

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 <- fit_lasso(
  x, Surv(time, event),
  nfolds = 5, rule = "lambda.1se", seed = 11
)

# External calibration
cal.ext <- calibrate_external(
  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))
# # Test fused lasso, MCP, and Snet models
# data(smart)
# # Use first 500 samples as training data
# # (the data used for internal validation)
# x <- as.matrix(smart[, -c(1, 2)])[1:500, ]
# time <- smart$TEVENT[1:500]
# event <- smart$EVENT[1:500]
#
# # Take 1000 samples as external validation 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]
#
# flassofit <- fit_flasso(x, survival::Surv(time, event), nfolds = 5, seed = 11)
# scadfit <- fit_mcp(x, survival::Surv(time, event), nfolds = 5, seed = 11)
# mnetfit <- fit_snet(x, survival::Surv(time, event), nfolds = 5, seed = 11)
#
# cal.ext1 <- calibrate_external(
#   flassofit, x, time, event,
#   x_new, time_new, event_new,
#   pred.at = 365 * 5, ngroup = 5)
#
# cal.ext2 <- calibrate_external(
#   scadfit, x, time, event,
#   x_new, time_new, event_new,
#   pred.at = 365 * 5, ngroup = 5)
#
# cal.ext3 <- calibrate_external(
#   mnetfit, x, time, event,
#   x_new, time_new, event_new,
#   pred.at = 365 * 5, ngroup = 5)
#
# print(cal.ext1)
# summary(cal.ext1)
# plot(cal.ext1)
#
# print(cal.ext2)
# summary(cal.ext2)
# plot(cal.ext2)
#
# print(cal.ext3)
# summary(cal.ext3)
# plot(cal.ext3)

road2stat/hdnom documentation built on March 14, 2024, 11:10 p.m.