Evaluation of machine-learning models to measure individualized treatment effects from randomized clinical trial data with time-to-event outcomes

Objective: In randomized clinical trials, prediction models can be used to explore relationships between patient variables and treatment effect magnitude. Our aim was to evaluate flexible machine learning models that incorporate interactions and nonlinear effects from high-dimensional data to estima