A framework for the rigorous assessment of heterogeneous treatment effects from a single randomized controlled trial

Randomized controlled trials are the gold standard for estimating the average effect of a treatment in a target population, but the same treatment may benefit some patients while having no effect on or even harming others. This phenomenon, termed heterogeneous treatment effects, can be quantified by estimating treatment effects within subgroups of patients, defined by various combinations of baseline covariates. One approach for quantifying heterogeneous treatment effects is to develop "effect models" that directly model complex interactions between baseline covariates and treatment assignment. "Effect scores," derived from effect models, can then be used to rank patients based on their predicted treatment benefit, enabling targeted treatment regimens. In this article, we provide a rigorous general framework for developing and evaluating effect models to characterize heterogeneous treatment effects from a single randomized control trial. We address challenges in valid model development, such as overfitting, and illustrate our approach in a real-world dataset with time-to-event outcomes subject to right-censoring.