Predictive approaches to heterogeneous treatment effects: a systematic review

Background Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. Methods We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. Results The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). Conclusion Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis. Key messages Heterogeneity of treatment effect refers to the non-random variation in the direction or magnitude of a treatment effect for individuals within a population. A large number of regression-based predictive approaches to the analysis of treatment effect heterogeneity exists, which can be divided into three broad classes based on if they incorporate: prognostic factors (risk-based methods); treatment effect modifiers (optimal treatment regime methods); or both (treatment effect modeling methods). Simulations and empirical evaluations are required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.