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Objective: To compare different risk-based methods for optimal prediction oftreatment effects. Methods: We simulated RCT data using diverse assumptions forthe average treatment effect, a baseline prognostic index of risk (PI), theshape of its interaction with treatment (none, linear, quadratic ornon-monotonic), and the magnitude of treatment-related harms (none or constantindependent of the PI). We predicted absolute benefit using: models with aconstant relative treatment effect; stratification in quarters of the PI;models including a linear interaction of treatment with the PI; modelsincluding an interaction of treatment with a restricted cubic spline (RCS)transformation of the PI; an adaptive approach using Akaike's InformationCriterion. We evaluated predictive performance using root mean squared errorand measures of discrimination and calibration for benefit. Results: Thelinear-interaction model displayed optimal or close-to-optimal performanceacross many simulation scenarios with moderate sample size (N=4,250 patients; ~785 events). The RCS-model was optimal for strong non-linear deviations from aconstant treatment effect, particularly when sample size was larger (N=17,000).The adaptive approach also required larger sample sizes. These findings wereillustrated in the GUSTO-I trial. Conclusion: An interaction between baselinerisk and treatment assignment should be considered to improve treatment effectpredictions.

Individualized treatment effect was predicted best by modeling baseline risk in interaction with treatment assignment