Journal of Data Science

Identifying treatment effect modifiers (i.e., moderators) plays an essential role in improving treatment efficacy when substantial treatment heterogeneity exists. However, studies are often underpowered for detecting treatment effect modifiers, and exploratory analyses that examine one moderator per statistical model often yield spurious interactions. Therefore, in this work, we focus on creating an intuitive and readily implementable framework to facilitate the discovery of treatment effect modifiers and to make treatment recommendations for time-to-event outcomes. To minimize the impact of a misspecified main effect and avoid complex modeling, we construct the framework by matching the treated with the controls and modeling the conditional average treatment effect via regressing the difference in the observed outcomes of a matched pair on the averaged moderators. Inverse-probability-of-censoring weighting is used to handle censored observations. As matching is the foundation of the proposed methods, we explore different matching metrics and recommend the use of Mahalanobis distance when both continuous and categorical moderators are present. After matching, the proposed framework can be flexibly combined with popular variable selection and prediction methods such as linear regression, least absolute shrinkage and selection operator (Lasso), and random forest to create different combinations of potential moderators. The optimal combination is determined by the out-of-bag prediction error and the area under the receiver operating characteristic curve in making correct treatment recommendations. We compare the performance of various combined moderators through extensive simulations and the analysis of real trial data. Our approach can be easily implemented using existing R packages, resulting in a straightforward optimal combined moderator to make treatment recommendations.