surv2binary: Convert a "Surv" object into binary variables at different...

In easy.glmnet: Functions to Simplify the Use of 'glmnet' for Machine Learning

Convert a "Surv" object into binary variables at different time points

Description

Function to convert a "Surv" object (e.g., the predictions obtained from glmnet_predict using a "cox" model) into a list of binary variables (e.g., as obtained from glmnet_predict using a "binomial" model) at different time points.

Usage

surv2binary(x, subset_id = NULL)

Arguments

x	a "Surv" object.
subset_id	vector or factor identifying the subsets for which the conversion will be performed separately.

Details

This function is useful, for instance, to estimate the AUC at different timepoints from "cox" predictions.

Value

A list of times and binary variables.

Author(s)

Joaquim Radua

References

Salazar de Pablo, G., Radua, J., Frearson, G., Young, A.H., Arango, C., Kelleher, I., Sharma, A., Uhlhaas, P.J., Solmi, M., Fusar-Poli, P., Guinart, D., Correll, C.U. (2025) Development and validation of a prognostic model and risk calculator for the estimation of bipolar-spectrum disorder risk in hospitalised adolescents with non-psychotic/non-bipolar mental disorders. Molecular Psychiatry, in Press, doi:10.1038/s41380-025-03244-1.

See Also

glmnet_predict for obtaining "cox" predictions. cv for conducting a cross-validation.

Examples

library(survival)
library(pROC)

# Create random x (predictors) and y (survival)
x = matrix(rnorm(5000), ncol = 10)
time = rexp(500)
y = Surv(time, plogis(x[,1] / pmax(1, time^2) + rnorm(500)) > 0.5)

# Predict y via cross-validation
fit_fun = function (x, y) {
  glmnet_fit(x, y, family = "cox")
}
predict_fun = function (m, x) {
  glmnet_predict(m, x)
}
res = cv(x, y, family = "cox", fit_fun = fit_fun, predict_fun = predict_fun)

# Convert y to binary
y.binary = surv2binary(y)

# Calculate and plot AUC for binary y at each timepoint
time_auc = NULL
for (i in 1:length(y.binary)) {
  status_i = y.binary[[i]]$status
  if (length(unique(na.omit(status_i))) == 2) {
    time_auc = rbind(time_auc, data.frame(
      time = y.binary[[i]]$time,
      auc = roc(status_i ~ res$predictions$y.pred, levels = 0:1, direction = "<")$auc
    ))
  }
}
plot(time_auc$time, time_auc$auc, type = "l", xlab = "Time", ylab = "AUC", ylim = 0:1)
abline(h = 0.5)

easy.glmnet documentation built on Feb. 8, 2026, 5:06 p.m.