Estimating individualized treatment effects from randomized controlled trials: a simulation study to compare risk-based approaches

• Published in: BMC medical research methodology Vol. 23; no. 1; p. 74
• Main Authors: Rekkas, Alexandros, Rijnbeek, Peter R, Kent, David M, Steyerberg, Ewout W, van Klaveren, David
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28-03-2023; BioMed Central; BMC
• Subjects: Absolute benefit; Computer Simulation; Evaluation; Heart attacks; Humans; Medical research; Methods; Patients; Prediction models; Prognosis; Random variables; Randomized Controlled Trials as Topic; Regression analysis; Sample Size; Simulation; Simulation methods; Treatment effect heterogeneity; Treatment outcome; Netherlands; Prediction models; Absolute benefit; Treatment effect heterogeneity
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-023-01889-6

Baseline outcome risk can be an important determinant of absolute treatment benefit and has been used in guidelines for "personalizing" medical decisions. We compared easily applicable risk-based methods for optimal prediction of individualized treatment effects.
We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk, the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the prognostic index). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the prognostic index; models including a linear interaction of treatment with the prognostic index; models including an interaction of treatment with a restricted cubic spline transformation of the prognostic index; an adaptive approach using Akaike's Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit.
The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size (N = 4,250; ~ 785 events). The restricted cubic splines model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger (N = 17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial.
An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions.