PrognosticResult: Compute Prognostic Results for Survival Models

In IOBR: Immune Oncology Biological Research

Compute Prognostic Results for Survival Models

Description

Computes and compiles prognostic results from a survival model fitted with 'glmnet'. Extracts model coefficients at optimal lambda values ('lambda.min' and 'lambda.1se') and calculates time-dependent AUC metrics for both training and testing datasets.

Usage

PrognosticResult(model, train.x, train.y, test.x, test.y)

Arguments

model	A fitted survival model object (e.g., from 'glmnet::cv.glmnet').
train.x	Matrix or data frame of training predictors.
train.y	Training dataset survival outcomes (time and status).
test.x	Matrix or data frame of testing predictors.
test.y	Testing dataset survival outcomes (time and status).

Value

A list containing:

model

The fitted model object

coefs

Data frame of coefficients at 'lambda.min' and 'lambda.1se'

AUC

Data frame with AUC values for train/test at both lambda values

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE) &&
  requireNamespace("timeROC", quietly = TRUE)) {
  library(survival)
  set.seed(123)
  train_x <- matrix(rnorm(100 * 10), ncol = 10)
  train_y <- data.frame(time = rexp(100), status = rbinom(100, 1, 0.5))
  test_x <- matrix(rnorm(50 * 10), ncol = 10)
  test_y <- data.frame(time = rexp(50), status = rbinom(50, 1, 0.5))
  fit <- glmnet::cv.glmnet(train_x, Surv(train_y$time, train_y$status), family = "cox")
  results <- PrognosticResult(
    model = fit, train.x = train_x, train.y = train_y,
    test.x = test_x, test.y = test_y
  )
}

IOBR documentation built on May 30, 2026, 5:07 p.m.